Senior AI Engineer

Spheresmith; Bengaluru; Hybrid; INR 300,000 - 600,000; Full time

Job summary

Spheresmith, a Decision Intelligence company, is seeking a Senior AI Engineer to drive architecture and deployment of agentic AI systems. You will build multi-agent workflows, integrate domain ontologies with GraphRAG, and fine-tune private LLMs for enterprise use with MCP protocols.

Join a production-focused team delivering transparent AI outputs, with robust tool integration, model evaluation, and scalable cloud deployments (GCP/AWS). Hybrid work in Bangalore, India.

Qualifications

• 5+ years of AI/ML software engineering with production deployments.
• Expertise with LangGraph, LangChain, LlamaIndex or other multi-agent tools.
• Strong knowledge of RAG systems, knowledge graphs, and graph databases.

Responsibilities

• Design and optimize stateful multi-agent systems.
• Integrate domain ontologies with Knowledge Graphs and GraphRAG.
• Fine-tune private LLMs using PEFT and LoRA/QLoRA.
• Architect hybrid RAG pipelines with citation tracking.
• Develop production-grade Python services with FastAPI and Pydantic.
• Deploy to cloud with Docker/Kubernetes.

Skills

LangGraph

LangChain

Multi-agent systems

Python

Pydantic

FastAPI

PEFT

GraphRAG

Knowledge Graphs

Tools

Neo4j

Memgraph

RDF/OWL ontologies

Weaviate

Qdrant

Pinecone

FAISS

Docker

Kubernetes

GCP/AWS

Job description

Job Title: Senior AI Engineer (Agentic AI, RAG & Knowledge Graphs)

Company: Spheresmith

Experience Level: Senior (5+ years in AI/ML & Software Engineering)

Job Title: Senior AI Engineer (Agentic AI, RAG & Knowledge Graphs)

Location: Hybrid / On-site (Bangalore, India)

Company: Spheresmith

Experience Level: Senior (5+ years in AI/ML & Software Engineering)

About Spheresmith

Spheresmith is a Decision Intelligence company that builds governed, accurate, and auditable decision systems aligned with each client’s policies and guidelines. Originating in investment management for LPs, GPs, and family offices, Spheresmith extends its platform across corporate finance, sales, marketing, and IT.

By combining domain ontologies, reusable decision records, and private, domain-fine-tuned LLMs, our product suite—including Investsphere, Prism, and Corpsphere—ensures sensitive data stays within customer environments while converting ad hoc choices into transparent, traceable decision lifecycles. Over 35 enterprise customers rely on Spheresmith to give every high-impact decision clear context, ownership, and supporting data.

Position Overview

We are seeking a Senior AI Engineer to drive the architecture, development, and deployment of our next-generation decision intelligence systems. In this role, you will build autonomous and semi-autonomous multi-agent workflows, integrate structured domain ontologies with unstructured data via GraphRAG, fine-tune private domain-specific LLMs, and implement robust tool integration protocols like Model Context Protocol (MCP).

You will work at the intersection of production software engineering, cutting-edge agent frameworks, and enterprise-grade data science to deliver transparent, reproducible AI outputs for high-stakes business decisions.

Key Responsibilities

• Agentic Architecture & Tool Orchestration: Design, implement, and optimize stateful multi-agent systems using LangGraph and LangChain. Build tool integration mechanisms via Model Context Protocol (MCP) and function calling to connect agents seamlessly with private enterprise databases, APIs, and microservices.
• Knowledge Graphs & Ontologies: Partner with domain experts to translate corporate guidelines and investment policies into formal domain ontologies. Integrate Knowledge Graphs (KGs) with vector retrieval (GraphRAG) to enable precise, auditable, and context-aware reasoning.
• Domain Fine-Tuning & Evaluation: Fine-tune open-weight LLMs (e.g., Llama 3, Mistral, Qwen) using parameter-efficient techniques (PEFT, LoRA/QLoRA) to run within private cloud environments. Establish rigorous evaluation frameworks (Ragas, TruLens, DeepEval) to guarantee accuracy and compliance.
• Advanced RAG Pipelines: Architect hybrid retrieval systems (dense vectors + sparse search + knowledge graphs) with re-ranking, query transformation, and strict citation tracking to eliminate hallucination.
• Data Science & Analytics: Conduct data analysis, embedding analysis, and model performance profiling to optimize retrieval accuracy, inference latencies, and output consistency across enterprise use cases.
• Enterprise AI Integration: Write production-grade, asynchronous Python code with Pydantic schemas, FastAPI endpoints, and clean microservice architecture. Deploy scalable workloads to cloud infrastructure (GCP/AWS) utilizing Docker/Kubernetes containerization.

Requirements

• Experience: 5+ years of experience in AI/ML software engineering with a strong track record of deploying LLM-powered applications into production environments.
• Agentic Frameworks: Hands-on mastery of LangGraph, LangChain, LlamaIndex, or custom multi-agent orchestration engines.
• Model Context Protocol & APIs: Deep understanding of MCP, standard function-calling paradigms, REST/gRPC APIs, and tool execution boundaries.
• RAG & Knowledge Graphs: Proven experience building advanced RAG systems. Hands-on experience with graph databases (Neo4j, Memgraph, RDF/OWL ontologies, networkx) and GraphRAG methodologies.
• LLM Fine-Tuning: Practical experience fine-tuning LLMs using Hugging Face tools, Unsloth, LoRA/QLoRA, and dataset curation/synthetic data generation.
• Core Engineering & Data Science: Expert-level Python (asyncio, Pydantic, FastAPI) alongside solid data science foundations (embeddings, vector indexing with FAISS/Qdrant/Pinecone, statistics, data preprocessing).
• Databases & Vector Stores: Hands-on work with vector engines (Weaviate, Qdrant, PGVector) and relational/document databases.