AI ML Engineer

AI / ML Engineer

Accenture in India is seeking a hands-on AI Native Engineer to design, build, and deploy end-to-end agentic and LLM-powered systems. This role involves constructing RAG pipelines, working with vector databases, building autonomous or semi-autonomous agents, integrating tools and skills, and developing evaluation frameworks for high-quality AI behavior. You will prototype rapidly, experiment with models, and evolve solutions from PoC to production while working closely with cross-functional teams.

Key Responsibilities

Agentic & LLM System Development

• Build agentic AI systems, including agent orchestration, planning loops, tool calling, and memory modules.
• Implement LLM toolchains, custom prompts, templates, evaluators, and multi-step reasoning workflows.
• Develop autonomous/semi-autonomous agents for retrieval, summarization, decision support, or workflow automation.

RAG Pipelines & Vector Intelligence

• Design and implement RAG pipelines end-to-end: ingestion, chunking, embeddings, indexing, vector search, hybrid retrieval, and grounding.
• Integrate vector databases such as pgvector, Pinecone, Weaviate, or Milvus.
• Optimize retrieval quality, latency, and factual accuracy using rerankers, retrieval evaluators, and freshness pipelines.

Model Integration & AI Ops

• Integrate enterprise-grade AI APIs, foundation models, and transformer models into scalable systems.
• Implement robust evaluation frameworks including offline and online evals, regression tests, content safety checks, and red-team scenarios.
• Build monitoring for model drift, agent failure modes, hallucination detection, and end-to-end system health.

Full-Stack & Cloud Alignment

• Deploy services using Python/Node/Java microservices, serverless functions, or containerized workloads.
• Integrate event streams, API gateways, and cloud-native patterns across Azure/AWS/GCP.
• Build CI/CD pipelines for AI services with safe rollouts, versioning, and feature flags for model updates.

Prototyping & Rapid Iteration

• Rapidly experiment with models, embeddings, architectures, and agentic patterns.
• Translate business requirements into AI-native technical architectures and communicate trade-offs via demos and deep-dives.
• Document designs, experiments, and evaluation results for reproducibility and knowledge sharing.

Key skills/competency

• Machine Learning
• AI Native Engineer
• Agentic AI Systems
• LLM
• RAG Pipelines
• Vector Databases
• Prompt Engineering
• Cloud AI
• Python
• Production Deployment