Gen AI Engineer_Danske

Infosys; Bengaluru; On-site; INR 800,000 - 1,500,000; Full time

Job summary

Infosys in Bengaluru is looking for a talented individual to design and develop Generative AI applications. Responsibilities involve building and optimizing RAG pipelines, collaborating with various teams, and ensuring best practices in model deployment.

Candidates must possess strong experience in Python and familiarity with Generative AI and LLMs. The role provides opportunities to work with cutting-edge technologies in the Data Analytics Unit.

Qualifications

• Strong hands-on experience in Python.
• Good experience with Generative AI / Large Language Models (LLMs).
• Solid understanding of NLP, embeddings, transformers, and prompt engineering.

Responsibilities

• Design and develop LLM-based applications for enterprise use cases.
• Build and optimize RAG pipelines using modern techniques.
• Collaborate with cross-functional teams to productionize AI use cases.

Skills

Python

Generative AI / LLMs

NLP

RAG pipelines

Docker

Kubernetes

Education

MCA, Bachelor of Engineering, BTech

Tools

AWS

Databricks

Vector databases (FAISS, Pinecone, etc.)

REST APIs

Job description

Educational Requirements

MCA, Bachelor of Engineering, BTech

Service Line

Data Analytics Unit

Responsibilities

1. Design and develop Generative AI / LLM-based applications for enterprise use cases such as chatbots, summarization, document intelligence, search, QA, code assistants, and workflow automation.
2. Build and optimize RAG (Retrieval-Augmented Generation) pipelines using embeddings, vector databases, chunking, retrieval strategies, and prompt orchestration.
3. Work with foundation models / LLMs from providers such as OpenAI, Anthropic, Cohere, Hugging Face, AWS Bedrock, or open-source models.
4. Develop robust backend services and APIs to integrate GenAI solutions into enterprise platforms and applications.
5. Fine-tune, evaluate, and improve model responses through prompt engineering, guardrails, grounding, and performance optimization.
6. Build scalable AI pipelines on AWS or Databricks for training, experimentation, deployment, and monitoring.
7. Collaborate with Data Science, ML Engineering, Application Engineering, and Product teams to productionize GenAI use cases.
8. Ensure best practices in model deployment, observability, security, governance, and responsible AI.
9. Contribute to architecture discussions, PoCs, reusable frameworks, and accelerators for GenAI adoption.

Additional Responsibilities

• Hands-on experience in either AWS or Databricks: AWS Bedrock, SageMaker, Lambda, ECS/EKS, S3, API Gateway, CloudWatch, IAM; experience deploying scalable AI/ML workloads on AWS; or Databricks notebooks, MLflow, Model Serving, Delta Lake, Unity Catalog.
• Exposure to MLOps / LLMOps concepts.
• Knowledge of model monitoring, evaluation, experimentation, and versioning.
• Experience with Docker, Kubernetes, CI/CD pipelines.
• Familiarity with guardrails, AI safety, content filtering, and governance.
• Exposure to multimodal AI, agentic workflows, or autonomous AI systems.
• Understanding of structured/unstructured data processing pipelines.

Technical and Professional Requirements

1. Strong hands-on experience in Python
2. Good experience with Generative AI / Large Language Models (LLMs)
3. Solid understanding of NLP, embeddings, transformers, prompt engineering, and LLM application design
4. Experience building RAG pipelines
5. Hands-on experience with orchestration frameworks such as LangChain / LlamaIndex / Semantic Kernel / AutoGen / CrewAI / LangGraph
6. Experience with vector databases such as FAISS, Pinecone, Chroma, Weaviate, Milvus, Elasticsearch/OpenSearch
7. Exposure to REST APIs / FastAPI / Flask for serving AI applications
8. Strong understanding of ML lifecycle, deployment, testing, and performance tuning

Preferred Skills

• Technology->Machine Learning->Generative AI->retrieval augmented generation (rag)
• Technology->Generative AI->Generative AI for Data Analytics
• Technology->data science->Databricks Machine Learning