Gen AI Developer/AI ML Engineer

CoRover ️ P. Limited; Bengaluru; On-site; INR 167,400 - 279,000; Full time

Job summary

A technology company is seeking a GenAI/AI-ML engineer to develop and manage LLM/RAG services. The candidate will be responsible for building workflows in Python, optimizing performance, and ensuring safe AI practices. Strong Python skills and hands-on experience with LLM applications are essential. This role also involves the integration of various AI capabilities into the company's platform and managing observability metrics. This position is a full-time opportunity based in Bengaluru, India.

Qualifications

• Strong Python skills, experienced with FastAPI and asyncio.
• Hands-on experience with LLM-powered applications.
• Knowledge of RAG architectures and vector databases.

Responsibilities

• Build core LLM/RAG services in Python.
• Implement agentic AI workflows.
• Stream server-side responses and manage prompt lifecycles.

Skills

Python skills

FastAPI

asyncio

Pydantic

LLM-powered applications

RAG architectures

vector databases

streaming APIs

AI safety

CI/CD pipelines

Job description

Overview

We are looking for a GenAI/AI‑ML engineer to build and own core LLM/RAG services, agentic workflows, and the integration layer that connects AI capabilities to our platform. You will work Python‑first, ship streaming APIs, manage prompt lifecycles, and ensure safety, observability, and performance at scale.

Key Responsibilities

• Build LLM/RAG services in Python (FastAPI/asyncio, Pydantic) with clean APIs and tests.
• Implement agentic AI workflows — tool‑using agents with planning, memory, multi‑step execution, and recovery/fallback paths.
• Stream responses server‑side from model to UI (SSE/WebSockets with retries, backoff, partial responses, and cancellation).
• Build RAG pipelines: ingestion, chunking, embeddings, indexing, reranking, and grounded answers with citations (pgvector/FAISS/Pinecone).
• Manage prompt versioning, templates/params, safe fallbacks, and rollbacks.
• Run evaluations: hallucination/groundedness checks, regression suites for prompts and retrievers.
• Implement guardrails: PII detection/redaction, content safety, and domain constraints.
• Optimise for performance and cost: context reduction, caching/batching, request pacing, and rate‑limit handling.
• Design and ship REST/gRPC endpoints that orchestrate tools, retrieval, and post‑processing (citations/formatting).
• Implement and consume MCP (Model Context Protocol) tool adapters — files, web, DB connectors — with capability negotiation, auth/permissions, and resource limits.
• Integrate vector stores (pgvector/FAISS/Pinecone) plus DB/file stores and enterprise connectors (SharePoint, Confluence, Slack, Jira).
• Own AuthN/Z and tenancy: JWT/OAuth, role/tenant isolation, secrets management, and audit logging for AI actions.
• Set up observability: logs, metrics, traces, and dashboards/alerts for latency, error rate, and token spend.
• Manage queues/workflows for background ingestion/summarisation with idempotency and retries.
• Enforce data governance: input/output validation, schema contracts, PII handling, and retention policies.
• Maintain CI/CD for prompts, retrieval configs, and API changes; support blue/green or canary releases.

Required Skills & Qualifications

• Strong Python skills — FastAPI, asyncio, Pydantic; clean, tested, production‑ready code.
• Hands‑on experience building LLM‑powered applications (OpenAI, Gemini, Llama, or similar).
• Experience with RAG architectures and vector databases (pgvector, FAISS, Pinecone, or similar).
• Familiarity with agentic frameworks (LangChain, LlamaIndex, CrewAI, or custom implementations).
• Knowledge of streaming APIs: SSE and WebSockets.
• Understanding of prompt engineering, prompt versioning, and evaluation techniques.
• Experience integrating REST/gRPC APIs and enterprise connectors.
• Awareness of AI safety, guardrails, PII handling, and responsible AI practices.
• Familiarity with observability tooling (OpenTelemetry, Grafana, Prometheus, or similar).
• Good understanding of CI/CD pipelines and DevOps practices in an AI/ML context.