Gen AI Developer
Tata Consultancy Services
2 - 5 years
Bengaluru
Posted: 01/01/2026
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Job Description
Role: Gen AI Developer
Required Technical Skill Set (Must Have): Gen AI / LLM /Agentic AI
Desired Experience Range:6 to 8 Yrs
Location of Requirement: PAN INDIA
Desired Competencies (Technical/Behavioral Competency)
Must-Have
- Strong programming skills in Python with experience in building ML/NLP applications.
- Hands-on experience with Deep Learning , Machine Learning , and Natural Language Processing .
- Proficiency in working with LLMs (e.g., GPT,Gemini, LLaMA, Mistral) and RAG architecture.
- Experience with Agentic AI frameworks and autonomous agent design.
- Familiarity with FastAPI for building RESTful APIs.
- Experience with CI/CD pipelines and deploying solutions on Azure Cloud, GCP and AWS
- Solid understanding of data structures, algorithms, and software engineering principles.
- Bachelors or masters degree in computer science, AI, Data Science, or related field.
- Retrieval Augmented Generation (RAG): Hands-on experience in building and optimizing RAG pipelines from scratch
- Expert-level proficiency and hands-on experience with LangChain and/or LlamaIndex for building complex LLM applications, including chains, agents, memory, and tool integration.
Good-to-Have
- Excellent Communication Skills
- Strong Software Engineering Background (Productionizing the models)
- Hands-on experience with data science tools
- Problem-solving aptitude
- Analytical mind and great business sense
Responsibility of / Expectations from the Role
- Design and implement GenAI solutions leveraging LLMs, RAG pipelines, and Agentic AI architectures.
- Develop and fine-tune deep learning models for NLP tasks using frameworks like PyTorch or TensorFlow.
- Build scalable APIs using FastAPI to serve AI models and integrate with enterprise systems.
- Collaborate with data scientists and ML engineers to deploy models using CI/CD pipelines on Azure.
- Optimize model performance and ensure robustness in production environments.
- Stay updated with the latest research and advancements in generative AI and deep learning.
- Scalability & Performance: Ability to write efficient code and consider scalability and latency implications for Gen AI applications.
- Containerization: Practical experience with Docker for packaging and deploying applications.
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