Senior Associate Forward Deployment Engineer (GenAI / Agentic AI Automation)

GenAI & Agentic AI Engineering | Forward Deployed Engineering

Experience : 5 to 9 years

Locations : Bangalore & Hyderabad

Job Summary : A senior engineer who owns AI solutions end to end inside a client's environment, from a vague problem to a production-grade deployment. As the technical owner, you will scope, architect, build, and ship GenAI and agentic solutions, and set the approach for evaluation and responsible AI.

Key Responsibilities

- Own AI solutions end to end, from discovery to a production-grade deployment on client infrastructure.
- Design agentic architectures and RAG pipelines that hold up at enterprise scale.
- Define the approach to prompt engineering, model evaluation, and responsible AI.
- Integrate solutions into enterprise systems and both structured and unstructured data.
- Make architecture, technology-selection, and design-review decisions.
- Work across the full stack to ship end to end.
- Mentor engineers and raise delivery capability.
- Codify reusable patterns and share field intelligence with the practice.

Required Qualifications

- Substantial software engineering experience with strong hands-on GenAI/agentic work in production.
- Deep experience with LLMs on AWS Bedrock and provider APIs, and with agent frameworks.
- Experience designing and deploying enterprise AI applications (agentic workflows, RAG, vector databases, embeddings, semantic search).
- A solid handle on prompt engineering, model evaluation,



and responsible AI in production.
- Strong full-stack engineering (Python and TypeScript, APIs) and data engineering fundamentals.
- Ability to navigate ambiguity and drive outcomes at pace.

Preferred Qualifications

- AWS AI services (Bedrock, SageMaker).
- Enterprise AI platforms (Palantir Foundry/AIP, Databricks, Snowflake Cortex).
- Production deployment (Docker, Kubernetes, CI/CD, Terraform).
- Experience in regulated industries.
- A track record as an early engineer or technical founder building from zero to production.
- AWS Certified Solutions Architect Skilled; an AI/ML certification such as AWS Certified Machine Learning – Specialty.

Technical Skills & Tools

- LLMs & AI: AWS Bedrock, Anthropic Claude, OpenAI; prompt engineering, model evaluation, responsible AI
- Agent frameworks & orchestration: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen; workflow orchestration
- Retrieval & RAG: embeddings, vector databases (Pinecone, pgvector, Weaviate, Qdrant, OpenSearch), semantic search, rerankers
- Evaluation & observability: evaluation harnesses, Langfuse, OpenTelemetry
- Languages: Python, TypeScript / JavaScript
- Backend & APIs: FastAPI, Node.js, microservices, REST, GraphQL
- Frontend: React, TypeScript
- Data: SQL / NoSQL, data pipelines, streaming (Kafka, Kinesis), structured and unstructured data
- Good to have: AWS SageMaker, Docker, Kubernetes, CI/CD, Terraform, Databricks, Snowflake, Palantir Foundry

📌 GenAI / Agentic AI Automation (Bengaluru)
🏢 PwC
📍 Bengaluru