An AI/ML Associate role at Nekko Tech in Hyderabad requires Python, PyTorch, Computer Vision, GenAI, and AWS. This complete job-readiness guide tells you exactly how to prepare, what your gaps are, and how DSA’s AI and Data Science program gets you there faster.
INTRODUCTION
There is an AI/ML Associate role open right now at Nekko Tech, Hyderabad.
The company builds autonomous digital coworkers powered by Agentic AI. They are not a traditional software shop. They are building the next generation of enterprise automation — and they are hiring someone at the 1 to 2 year experience level to do real, production-grade AI/ML work from day one.
The job description is specific. Python proficiency is non-negotiable. Computer Vision using YOLO and OpenCV is required. PyTorch or TensorFlow. FastAPI or Flask for API development. AWS — Lambda, S3, EC2. GenAI exposure including LangChain or LlamaIndex. Vector databases. Clean Git repositories.
If you read that and felt a gap open up somewhere in your chest — this guide is written for you.
Not to overwhelm you. To show you exactly where you stand, what you are missing, and the fastest structured path from where you are today to a profile that a company like Nekko takes seriously.
Data Science Academy’s Level 1 and Level 2 AI and Data Science programs were built around exactly this kind of role profile. Not around what looks good in a course brochure — around what hiring managers at companies building production AI systems are actually looking for.
By the time you finish this guide, you will have a skills gap map, a preparation roadmap, answers to the questions your audience segment is actually searching for, a resume framework, and a free gap assessment you can complete today.
SECTION 1: WHAT NEKKO IS ACTUALLY BUILDING AND WHY IT MATTERS FOR YOUR PREPARATION
Before you prepare for a role, understand what the company does. It changes how you interpret every requirement on the JD.
Nekko is not building dashboards or analytics reports. They are building autonomous AI agents — systems that can perceive inputs, make decisions, and execute workflows without human intervention at every step. Their proprietary TensAI Platform and Nekko Iris are enterprise-grade infrastructure for this.
When they say they want someone who can develop Computer Vision models, build data engineering pipelines, wrap models into APIs, and deploy on AWS — they mean all of that in the context of an intelligent agent that needs to work reliably at scale in a real enterprise environment.
This context matters for your preparation because it tells you what kind of thinking they are testing for. They are not looking for someone who can follow a tutorial. They are looking for someone who understands why each piece of the pipeline exists, how the components connect, and what breaks when one of them fails in production.
Every skill on this JD is a component in that larger system. Prepare for them that way.
SECTION 2: THE JD DECODED — WHAT EACH REQUIREMENT ACTUALLY MEANS
PYTHON — CLEAN, PROFESSIONAL, NON-NEGOTIABLE
The JD says Python proficiency is a MUST with clean coding practices. That last part — clean coding practices — is doing significant work in that sentence.
They are not asking whether you can write Python. They are asking whether your code is readable, modular, commented, and structured in a way that another engineer can pick up and work with without asking you to explain it. In a collaborative AI team building production systems, messy code is not just inefficient — it is a liability.
What this means for your preparation: write code as if someone else will maintain it. Use docstrings. Name variables clearly. Structure your projects with proper directories, a requirements file, and a README that explains what the project does and how to run it. This is what your GitHub needs to demonstrate before you send an application.
COMPUTER VISION — YOLO, OPENCV, PYTORCH/TENSORFLOW
This is the most technically specific requirement on the JD and the one that will eliminate the largest proportion of applicants who come from a general data science background without CV specialisation.
YOLO — You Only Look Once — is the dominant real-time object detection framework. Understanding how to implement and fine-tune a YOLO model for a specific detection task, evaluate it on custom data, and optimise it for inference speed is the core skill here.
OpenCV is the foundational computer vision library for image preprocessing, contour detection, edge detection, morphological operations, and connecting raw image inputs to model pipelines.
PyTorch is the industry preference for research and production CV work. TensorFlow is acceptable and widely used. Knowing one well enough to implement, train, and fine-tune a convolutional architecture — not just call a pretrained model — is what this role requires.
What this means for your preparation: you need at least one complete Computer Vision project. Not a MNIST digit classifier. A real task — object detection on a custom dataset using YOLO, OCR pipeline using OpenCV and Tesseract, or a classification model trained from scratch or fine-tuned from a pretrained backbone. Documented, deployed or demonstrable, on GitHub.
GENAI EXPOSURE — LANGCHAIN, LLAMAINDEX, PROMPT ENGINEERING, LLMS
This is where the role moves from traditional ML into the frontier that every serious AI company is now building on.
LangChain and LlamaIndex are orchestration frameworks for building applications on top of large language models. They allow you to connect LLMs to external data sources, tools, and memory systems — which is exactly what autonomous AI agents require.
Prompt engineering is not a soft skill here. It is a technical discipline. Writing prompts that reliably produce structured, parseable, consistent outputs from LLMs is something you build through deliberate practice, not casual ChatGPT use.
What this means for your preparation: build one GenAI project. A RAG — Retrieval Augmented Generation — application using LlamaIndex or LangChain that connects an LLM to a document corpus and returns grounded answers is both achievable and impressive. It demonstrates LLM integration, vector database usage, and the orchestration thinking that Nekko’s work is built on.
AWS — LAMBDA, S3, EC2, BEDROCK
Cloud deployment is no longer optional for AI/ML roles. It has not been for several years. What has changed is the specificity of what companies expect even at the associate level.
AWS Lambda for serverless model serving — deploying a model as a function that runs on demand without managing a server. S3 for data storage and model artefact management. EC2 for training runs on GPU instances. Bedrock for accessing foundation models through AWS’s managed API.
You do not need to be a DevOps engineer. You need to be able to take a model you have trained, package it, and serve it through AWS infrastructure in a way that another engineer — or a downstream system — can call reliably.
What this means for your preparation: deploy something. Even a basic FastAPI application serving a scikit-learn model through an AWS Lambda function demonstrates the deployment mindset. Pair that with an S3 bucket for model storage and you have a working MLOps prototype that most applicants at this level cannot show.
VECTOR DATABASES — FAISS, PINECONE
Vector databases are the infrastructure layer under most modern RAG and semantic search applications. FAISS is the open-source option — fast, local, excellent for prototyping and smaller-scale applications. Pinecone is the managed cloud service — production-grade, scalable, used by companies building real AI products.
Understanding how embeddings work, how to index them, and how to query a vector store for semantic similarity is a skill that is now appearing consistently across AI/ML JDs and will only become more central as agentic AI systems mature.
API DEVELOPMENT — FASTAPI, FLASK
Wrapping models into APIs is the bridge between a data science project and a production system. FastAPI is the current industry preference for ML model serving — it is fast, type-safe, and auto-generates documentation. Flask is widely used and acceptable.
The expectation here is not full-stack web development. It is the ability to take a trained model, write a clean REST endpoint that accepts inputs and returns predictions, handle basic error cases, and document the endpoint clearly.
GIT AND DOCUMENTATION
This appears at the end of the JD but do not underestimate it. In a team building production AI systems, undocumented code and poor Git hygiene are genuine problems that slow everyone down. Clean commit history, meaningful commit messages, proper branching, and a well-structured repository README are not extras. They are professional standards.
SECTION 3: THESE ARE THE QUESTIONS THAT CANDIDATES AT THE 0 -2 YEAR EXPERIENCE LEVEL SEARCH FOR MOST CONSISTENTLY, WHEN PREPARING FOR AI/ML ROLES IN KERALA AND BEYOND
HOW DO I GET AN AI ML JOB IN INDIA WITH 1 YEAR OF EXPERIENCE?
The honest answer is that the experience requirement on most AI/ML JDs at the associate level can be partially substituted with a strong portfolio. Companies listing 1 to 2 years of experience are looking for demonstrated ability — they want proof that you have worked through real problems, made real decisions, and produced real results. If your portfolio shows that, the exact source of the experience matters less than the evidence of it. One year of structured training with two complete end-to-end projects is competitive against two years of tangential work experience.
WHAT PYTHON SKILLS DO I NEED FOR AN AI ML ASSOCIATE ROLE?
Beyond syntax fluency you need: pandas and NumPy for data manipulation, scikit-learn for the standard ML workflow, PyTorch or TensorFlow for deep learning, FastAPI or Flask for API development, and the ability to write modular, documented, reusable code. Object-oriented programming basics. Understanding of virtual environments, dependency management, and how to structure a Python project. The code quality matters as much as the content.
IS COMPUTER VISION A REQUIRED SKILL FOR AI/ML JOBS IN 2026?
For roles at companies building visual AI — OCR, document processing, object detection, industrial inspection, autonomous systems — yes, Computer Vision is not optional. For roles in NLP-heavy or tabular data companies, it may not appear at all. The Nekko role specifically lists CV as a core requirement because their enterprise automation products handle document and image inputs. If you are targeting roles at AI-native product companies, CV exposure gives you a significant edge even when it is not the primary focus.
HOW DO I LEARN LANGCHAIN AND LLAMAINDEX FOR AI JOBS?
Start with the official documentation, not YouTube tutorials that are often six months out of date in a framework moving this fast. Build a RAG application from scratch — choose a document set, generate embeddings using OpenAI or a Hugging Face model, store them in FAISS, and build a query interface using LangChain or LlamaIndex. That one project teaches you more than twenty tutorials. Then extend it — add memory, add tool use, add a web interface. The framework knowledge compounds quickly once you have the foundation.
WHAT IS AGENTIC AI AND HOW DO I PREPARE FOR JOBS IN IT?
Agentic AI refers to systems where an AI model — typically an LLM — can plan, decide, and take actions autonomously rather than just responding to prompts. An AI agent can call external tools, browse the web, write and execute code, and chain multiple steps together to complete a goal. Preparing for roles in this space means understanding LLM orchestration through LangChain or LlamaIndex, knowing how to connect models to external APIs and databases, and understanding the evaluation challenges of systems that make multi-step decisions. This is the fastest-growing area in enterprise AI and the skills are buildable within a structured program.
DO I NEED AWS CERTIFICATION TO GET AN AI ML JOB IN INDIA?
A certification helps but is not required at the associate level. What matters more is demonstrated hands-on experience — having deployed a model on AWS, having used S3 for data storage in a real project, having triggered a Lambda function. An AWS certification without project evidence carries less weight than a GitHub repository showing a deployed ML application with proper infrastructure. If you have time for both, do both. If you have to choose, build the project first.
HOW IMPORTANT IS A GITHUB PORTFOLIO FOR AI ML JOBS IN INDIA?
The Nekko JD explicitly says to submit a GitHub portfolio link alongside your resume. This is not a bonus request — it is a screening requirement. Candidates without a strong GitHub are filtered before they reach the interview stage. Your GitHub needs to show: clean, commented code; project READMEs that explain what you built and why; evidence of version control discipline through meaningful commit history; and at least one project that demonstrates end-to-end thinking from data to deployed output.
WHAT IS THE DIFFERENCE BETWEEN FAISS AND PINECONE FOR VECTOR SEARCH?
FAISS — Facebook AI Similarity Search — is an open-source library you run locally or on your own server. It is fast, free, and excellent for prototyping and smaller-scale applications. Pinecone is a managed cloud service that handles the infrastructure for you — scaling, indexing, and querying at production volume without you managing servers. For learning and portfolio projects, FAISS is the right starting point. For production applications, Pinecone or similar managed vector stores are the industry standard. Knowing both and being able to explain the tradeoff demonstrates the kind of systems thinking that senior engineers look for in associate-level candidates.
CAN A FRESHER APPLY FOR AI ML ASSOCIATE ROLES IN TRIVANDRUM, KOCHI, BANGALORE, HYDREBAD, CHENNAI?
The Nekko role asks for 1 to 2 years of experience. A fresher with a strong portfolio demonstrating real project work in CV, ML pipelines, GenAI, and API development can compete with candidates who have 1 year of work experience in a tangentially related role. The application will be screened, so the resume and GitHub need to do significant work. But the honest answer is yes — with the right preparation and evidence of practical capability, the experience requirement at the associate level is not an absolute barrier.
WHAT SALARY CAN I EXPECT FOR AN AI ML ASSOCIATE ROLE IN HYDERABAD?
For a 1 to 2 year experience AI/ML Associate role at a product company in Hyderabad in 2025, the typical range is between 6 and 12 LPA depending on the company stage, funding, and specific skill depth. At an AI-native startup like Nekko that is scaling aggressively, the equity component and growth trajectory may compensate for a lower base compared to a larger organisation. The specific number is negotiable and depends on your demonstrated skill level, portfolio quality, and interview performance — not the number of years on your resume.
SECTION 4: THE PREPARATION ROADMAP — DSA LEVEL 1 AND LEVEL 2
This is not a generic roadmap. It is mapped directly to the Nekko JD requirements and to what DSA’s AI and Data Science programs build in sequence.
LEVEL 1 — AI AND DATA SCIENCE FOUNDATIONS
This is where every serious preparation begins regardless of your starting point.
Python for Data Science — not just syntax but professional coding practices. Pandas, NumPy, data manipulation, exploratory analysis, and visualisation. The goal is fluency — you stop thinking about the language and start thinking about the problem.
Statistics and Probability — the mathematical foundation that makes every model decision defensible. Distributions, hypothesis testing, correlation, regression assumptions, Bayesian thinking basics. This is what separates candidates who can explain their model choices from candidates who cannot.
Machine Learning Fundamentals — supervised and unsupervised learning, model evaluation, feature engineering, cross-validation, bias-variance tradeoff. scikit-learn as the working toolkit. At least two complete ML projects — one classification, one regression — on real messy datasets.
SQL and Data Engineering Basics — professional SQL including joins, window functions, and subqueries. Introduction to data pipeline thinking — how data moves, how it gets cleaned, how it gets versioned.
Git and Project Structure — version control discipline, repository management, README writing, clean commit practice. By the end of Level 1 your GitHub should already be showing evidence of structured, professional work.
Introduction to Computer Vision — OpenCV fundamentals, image preprocessing, basic contour and edge detection. Introduction to CNNs through PyTorch. First exposure to YOLO for object detection.
LEVEL 2 — ADVANCED AI AND PRODUCTION READINESS
This is where candidates move from foundational knowledge to production-grade capability — the level Nekko is actually hiring for.
Advanced Computer Vision — fine-tuning YOLO on custom datasets, building OCR pipelines using OpenCV and Tesseract, classification architectures from scratch and via transfer learning. One complete CV project that is documented, demonstrable, and on GitHub.
Generative AI and LLM Integration — how large language models work, prompt engineering as a technical discipline, LangChain and LlamaIndex for orchestration, building a RAG application from document ingestion to query response. Vector databases — FAISS for prototyping, Pinecone for production-scale thinking.
Cloud Deployment and MLOps — AWS fundamentals: S3 for storage, EC2 for compute, Lambda for serverless serving. Deploying a model as a FastAPI application. Basic monitoring and logging for production ML systems. Introduction to Databricks for data engineering at scale.
API Development — FastAPI for model serving, building clean REST endpoints, input validation, error handling, auto-generated documentation. Wrapping a trained model into an API that another system can call reliably.
Agentic AI Fundamentals — how AI agents plan, decide, and act. Building a simple autonomous agent using LangChain’s agent framework. Tool integration — connecting an LLM to a search tool, a calculator, a database. Understanding the evaluation challenges of multi-step AI systems.
End-to-End Capstone Project — one complete project that touches every layer of the stack: data engineering pipeline, trained ML or CV model, API serving layer, cloud deployment, documentation. This becomes the anchor of your portfolio and the foundation of every interview answer.
DSA’s Level 1 and Level 2 programs are structured so that by the time you complete Level 2, you have the skills, the portfolio, and the interview vocabulary to compete for roles exactly like Nekko’s AI/ML Associate opening.
SECTION 5: YOUR RESUME FOR AN AI/ML ASSOCIATE ROLE
A resume that leads with “Seeking a challenging position in the field of Artificial Intelligence and Machine Learning to utilise my skills and contribute to organisational growth.”
A skills section that lists Python, Machine Learning, Deep Learning, NLP, Computer Vision, AWS, Docker, Kubernetes, Spark, and Scala — for a candidate who has been studying for eight months.
A projects section that says “Developed a machine learning model using Python” with no information about the problem, dataset, approach, evaluation metric, or result.
Certifications from five different platforms listed before any evidence of actual work.
WHAT WORKS
A two-line positioning summary that reads like this: “AI/ML engineer with hands-on experience in Computer Vision — YOLO, OpenCV — and end-to-end model deployment on AWS. Built a production-ready OCR pipeline and a RAG application using LangChain. Open to AI-native product environments where models ship to real users.”
A projects section that reads like this: “Built an object detection pipeline using YOLOv8 fine-tuned on a custom industrial inspection dataset of 4,200 annotated images. Achieved 91% mAP at IoU 0.5. Wrapped model in a FastAPI endpoint and deployed via AWS Lambda. Repository, documentation, and demo video at [GitHub link].”
A skills section that is honest and specific — not every tool you have ever heard of, but the tools you have actually used in a documented project.
Education listed below projects if you have project work to lead with.
SECTION 6: FREE RESUME REVIEW AND SKILLS GAP ASSESSMENT
Before you apply to a role like Nekko’s AI/ML Associate opening — or any AI/ML role — know exactly where you stand.
Answer these questions honestly. They map directly to what a hiring manager at a company like Nekko is looking for when they open your application.
- One — Do you have a GitHub profile with at least one complete Computer Vision project using YOLO or OpenCV that is documented well enough for a stranger to run it?
- Two — Have you built and deployed an ML model through an API endpoint — FastAPI or Flask — even on a local server or a free cloud tier?
- Three — Can you explain the difference between FAISS and Pinecone, when you would use each, and demonstrate a working vector search implementation in your portfolio?
- Four — Have you built any GenAI application using LangChain or LlamaIndex — a RAG pipeline, a basic agent, a prompt-chained workflow?
- Five — Have you used AWS for any part of a project — S3 for storage, Lambda for serving, EC2 for a training run?
- Six — Is your GitHub showing clean, commented, modular code with meaningful commit history and a structured README on every project?
- Seven — Can you walk through a complete end-to-end ML project in under five minutes, explaining the problem, the data, your decisions, and the business output — without referring to notes?
- If you answered yes to six or seven of these, you are close to application-ready for a role at this level. Apply, polish your resume, and get into interview prep.
- If you answered yes to four or five, you have real foundations. Three to four months of structured Level 2 work will close the remaining gaps.
- If you answered yes to fewer than four, you are at the beginning of the journey. That is not a problem — it is a starting point. DSA’s Level 1 program is designed exactly for where you are right now.
SECTION 7: CONCLUSION
The Nekko role is real. The company is building infrastructure for autonomous AI — one of the most consequential technical areas of this decade. They are hiring at the associate level, which means they expect to invest in someone with the right foundations and the right mindset, not someone who has already done this exact job at another company.
That is the window.
The skills they are asking for are learnable. The portfolio they are asking for is buildable. The preparation path is structured and available.
The only variable is whether you close the gap with the right guidance or spend the next eighteen months figuring it out alone.
DSA’s Level 1 and Level 2 AI and Data Science programs exist so that the answer to that question is not the thing standing between you and the career you are trying to build.
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