Role: AI/ML Engineer – GenAI & LLM Applications

Function: Artificial Intelligence / Machine Learning Engineering

Location: Bangalore ( Hybrid)

Type: Full-time

Industry: Information Technology & Services, Management Consulting

About Company

The company is a digital engineering firm founded in 2020, headquartered in Tampa, Florida. It specializes in AI-driven digital transformation for large enterprises.

Over 450 professionals work across seven global offices, spanning more than 25 countries. The firm delivers platform engineering, data analytics, supply chain transformation, and digital customer experience solutions.

In 2023, it expanded its product engineering capabilities through a strategic acquisition. It is a fast-paced, ownership-driven environment where engineers ship work at enterprise scale.

Position Overview

This role embeds directly within a client's AI product team, with end-to-end ownership of LLM application development from architecture through production. The engineer will drive RAG pipeline design, agentic workflow development, model fine-tuning, and AI safety implementation — with output shipping directly into the client's product at enterprise scale. It requires someone equally comfortable making architectural decisions independently and collaborating closely with client-side product and data engineering stakeholders.

Role & Responsibilities

• Embed with client product and engineering teams to architect and ship production-grade LLM-powered features end-to-end
• Build and optimize RAG pipelines with advanced chunking strategies, hybrid search, re-ranking, and vector database management (Pinecone, Milvus, Qdrant, or ChromaDB)
• Develop multi-agent systems and autonomous workflows with tool use, self-correction, and complex task execution using LangGraph, CrewAI, or equivalent agentic frameworks
• Fine-tune open-source LLMs (LLaMA, Mistral, or equivalent) using LoRA/QLoRA and implement 4-bit/8-bit quantization for cost-effective client deployment
• Set up and maintain production AI infrastructure including vLLM-based model serving, containerized deployments via Docker/Kubernetes, and continuous evaluation pipelines
• Implement AI safety and guardrail layers to mitigate hallucinations, enforce PII data protection, and monitor token usage and inference costs within client environments
• Transform raw, unstructured client data into high-value AI features in close collaboration with client-side Data Engineering and Product teams

Must Have Criteria

• 5+ years of Python engineering experience with production REST API development using FastAPI or Flask
• 2+ years of hands-on LLM application development using LangChain, LlamaIndex, or LangGraph shipped to production
• Demonstrated experience building and optimizing RAG pipelines including hybrid search, re-ranking, and vector DB management (Pinecone, Milvus, Qdrant, or ChromaDB)
• Hands-on experience fine-tuning open-source models (LLaMA, Mistral, or equivalent) using LoRA/QLoRA with Hugging Face Transformers and PyTorch
• Experience deploying and serving LLMs in production using Docker, Kubernetes, and vLLM or equivalent serving frameworks
• Working knowledge of AWS Bedrock or Google Vertex AI for managed model deployment and inference
• Experience with observability and evaluation tooling — LangSmith, Weights & Biases, or Arize Phoenix — in a live production AI context

Nice to Have

• Prior experience working in a client-facing or consulting delivery model where AI solutions were scoped, built, and handed off to enterprise clients
• Experience building multi-agent systems using CrewAI, AutoGPT, or custom function-calling/tool-use architectures
• Familiarity with AI safety frameworks, PII redaction pipelines, or responsible AI governance practices at the enterprise level
• Exposure to supply chain, ERP, or data platform modernization use cases
• AWS or GCP professional-level certification

What We Offer

• Hybrid work model — flexibility to work remotely with structured in-office collaboration
• Direct ownership of AI features shipping into live enterprise client products — visible impact from day one
• Global collaboration across seven offices with exposure to diverse enterprise domains and client challenges