Senior AI/ML Engineer

First Object; Dadri, Bengaluru; Hybrid; INR 2,400,000 - 4,200,000; Full time

Job summary

First Object seeks a Senior AIML Engineer to lead the LLM and agent layer for an enterprise financial automation platform. You will own design and implementation of LangChain/LangGraph-based workflows, integrating Azure OpenAI and other frontier LLMs, with a focus on traceability, auditability, and production-grade quality.

The role is hybrid, with strict emphasis on observability, governance, and scalable RAG pipelines.

Qualifications

• 6+ years of total software engineering experience with 3+ years building and shipping production-grade LLM-based applications.
• Proven experience building production-grade agents using LangChain, LangGraph, and LangServe.
• Familiarity with MCP and A2A agent protocols and other SDKs such as Google ADK, AutoGen, CrewAI, or Semantic Kernel.
• Experience with Azure OpenAI or frontier LLM provider APIs including structured outputs, function calling, token management, and latency optimization.
• RAG pipeline development with hands-on experience in vector databases and embedding pipelines.
• Prompt lifecycle management and eval pipelines, including versioning and environment-specific configuration.

Responsibilities

• Design and build LLM-based agent workflows using LangChain and LangGraph, including multi-step graphs and state management.
• Integrate and optimize Azure OpenAI or other frontier LLM providers, managing prompts, token usage, latency and model versions.
• Build and maintain RAG pipelines with vector databases and retrieval-augmented generation for document reasoning.
• Own LLM observability and evaluation harnesses, track confidence scores, and detect model regressions.
• Collaborate on agent orchestration patterns and inter-agent communication contracts with architects.
• Contribute to confidence thresholds, auto-resolve vs. human-escalation boundaries, and auditable outputs.

Skills

LangChain

LangGraph

LangServe

Agent workflows

Python

PostgreSQL

Kubernetes

Docker

Azure OpenAI

RAG pipelines

Observability

Tools

pgvector

Pinecone

Weaviate

Qdrant

FAISS

Langfuse

Prompt engineering

Job description

Senior AIML Engineer

Experience: 6+ Years

Location: Bangalore & Noida

Mode of Work: Hybrid

Summary

We are looking for a Senior AI/ML Engineer to join an Agent Development Squad building AI-powered financial close automation. You will own the LLM and agent layer, designing and building agent workflows using LangChain/LangGraph, integrating with Azure OpenAI or other frontier LLMs, and ensuring every agent action is traceable, evaluable, and production-grade. This is a hands-on engineering role, not a research role. The platform is Python-first, and you will work alongside backend and frontend engineers in a cross-functional squad.

What You'll Do

• Design and build LLM-based agent workflows using LangChain and LangGraph, covering multi-step graphs, tool use, agent handoffs, and state management.
• Integrate and optimize Azure OpenAI or other frontier LLM providers (Anthropic Claude, Google Gemini, etc.), managing prompt design, token usage, latency, and model versioning.
• Build and maintain RAG pipelines covering document ingestion, embedding generation, vector search using pgvector or equivalent vector database, and retrieval-augmented generation for financial document reasoning.
• Own LLM observability using Langfuse, instrument agent traces, build evaluation harnesses, track confidence scores, and detect model regressions.
• Collaborate with the Software Engineering Architect on agent orchestration patterns and inter-agent communication contracts.
• Contribute to confidence threshold calibration, defining the auto-resolve vs. human-escalation boundary per agent.
• Ensure all agent actions produce structured, auditable outputs compatible with the platform's SOX-compliant governance framework.

Who You Are

AI / LLM Engineering - Primary Gate

• 6+ years of total software engineering experience with 3+ years building and shipping production-grade LLM-based applications.
• Proven experience building production-grade agents, both assistive (human-in-the-loop, approval-driven) and ambient (autonomous, background execution), using LangChain, LangGraph, and LangServe; knows when to use each and how they compose.
• Familiarity with agent communication protocols, specifically Model Context Protocol (MCP) and Agent-to-Agent (A2A) and working knowledge of other Agent Development Kits such as Google ADK, AutoGen, CrewAI, or Semantic Kernel.
• Experience integrating with Azure OpenAI or other frontier LLM provider APIs (Anthropic Claude, Google Gemini, Mistral, etc.), including structured outputs, function calling, token management, and latency optimization.
• RAG pipeline development with hands-on experience in at least one vector database (pgvector, Pinecone, Weaviate, Qdrant, FAISS, or equivalent), covering embedding pipelines and retrieval strategies.
• Prompt lifecycle management and eval pipelines, covering versioning, rollback, environment-specific configuration, and running evaluation harnesses to regression-test prompt changes and validate RAG output.
• Experience building reliable production agent systems with input/output guardrails, confidence thresholding, fallback handling, and full traceability of every decision and tool call via Langfuse or equivalent.

Backend Engineering

• Python is your primary language; proficient with FastAPI or equivalent async frameworks for building agent service APIs.
• PostgreSQL with pgvector or equivalent vector database (Pinecone, Weaviate, Qdrant, FAISS), including schema design for agent state, audit logs, and vector search.
• Docker and Kubernetes for containerized service deployment in production environments.
• Azure DevOps CI/CD for integrating AI pipelines into automated build and deployment workflows.

Nice to Have

• Experience with financial domain data such as accounting entries, reconciliations, accruals, or variance analysis.
• Familiarity with SOX/GDPR compliance requirements for AI decision audit trails.
• Contributions to open-source LLM or agent framework projects.
• Experience with federated learning or model fine-tuning pipelines.

What You'll Learn & Gain

• End-to-end ownership of the LLM and agent layer on a live enterprise financial platform used by global customers.
• Deep hands-on experience with LangChain/LangGraph, Azure OpenAI and other frontier LLMs, agent protocols (MCP, A2A), and Langfuse in a production context where quality, auditability, and latency all matter.
• Exposure to building AI in a high-compliance, SOX-governed environment, balancing model performance with explainability and audit requirements.
• Collaboration with backend, frontend, and platform engineers in a squad model where your technical decisions directly shape product behavior.

Interested candidates are requested to share their updated CVs at [e-mail] for further consideration.