AI & Data Solution Engineer - Agentic AI

Job Title : AI & Data Solutions Engineer - Agentic AI

Experience : 8+ years

Location : Hyderabad / Bangalore

Job Description :

The AI and Data Solutions Engineer is a hands-on delivery engineering role within a federated hub-and-spoke model for a regulated financial services environment.

In this role, you will work closely with a Lead Engineer to build, harden, and scale components of an enterprise Agentic AI platform integrating autonomous AI workflows, data pipelines, and cloud services while maintaining compliance, security, and governance standards.

Key Responsibilities & Job Description :

• Agentic AI & LLM Systems : Develop and harden components for autonomous AI agent frameworks, retrieval-augmented generation (RAG) pipelines, vector databases, and multi-agent workflows.

• Data Engineering & Integration : Design and optimize enterprise-scale data pipelines, ETL processes, and modern data architecture (data warehouses, data lakes) to serve unstructured and structured data to AI applications.

• Production Hardening & Governance : Harden AI/ML components for high reliability, security, latency, and regulatory compliance required by financial services environments.

• Hub-and-Spoke Collaboration : Work with central AI hubs and business-line spokes to standardize core AI platforms, share reusable design patterns, and streamline deployments.

• CI/CD & MLOps Deployment : Containerize and deploy AI models and agents using enterprise cloud infrastructure, version control, and automated testing pipelines.

Essential Technical & Soft Skills1. Agentic AI & Modern Frameworks :

• Agentic Frameworks : LangChain, LangGraph, AutoGen, CrewAI, or LlamaIndex for agent orchestration.

• LLM Architectures & Retrieval : Vector DBs (ChromaDB, Pinecone, Qdrant, Milvus), RAG pattern optimization, prompt engineering, and function calling.

• Evaluation & Guardrails : LLM benchmarking, semantic guardrails (NeMo Guardrails, Llama Guard), and toxicity/bias testing for regulated finance setups.

Core Data & Cloud Engineering :

• Languages : Advanced Python, SQL, and PySpark/Scala.

• Data Platforms & Storage : Snowflake, Databricks, AWS (S3, Redshift), Azure, or GCP data lakes/warehouses.

• Data Processing : Enterprise ETL/ELT pipeline tools, Apache Spark, Kafka, or Airflow.

Software Engineering & MLOps :

• Containerization & CI/CD : Docker, Kubernetes, Git, and automated DevOps workflows.