Lead Automation Engineer

Job Title: Lead I - Data Science

Role:AI Automation Engineer

Location: Bengaluru, Karnataka, India

Employment Type: Full time

Experience: 5+ Years

Budget: Max up to 22LPA

Primary Skills:

• AWS
• Python
• SQL
• Large Language Models (LLMs)
• Graph Databases
• AI Automation
• MCP Server

Role Overview

We are looking for an experienced AI Automation Engineer to design, build, and deploy AI-driven automation solutions across business processes. The ideal candidate should have strong hands-on experience with Large Language Models, prompt engineering, MCP server creation, Python, SQL, AWS, and graph databases such as AWS Neptune. The role requires a strong combination of AI engineering capability and business understanding to convert business problems into scalable, secure, production-ready automation solutions.

Key Responsibilities

• Design, develop, and deploy AI automation solutions using LLMs and generative AI technologies.
• Build, configure, and integrate Model Context Protocol (MCP) servers and tools for connecting LLMs with enterprise systems, APIs, databases, and workflows.
• Develop prompt engineering strategies for reliable, reusable, and production-grade AI workflows.
• Create AI agents, automation pipelines, and orchestration flows for business process automation.
• Work with business stakeholders to understand requirements and translate them into AI-enabled technical solutions.
• Build integrations with enterprise systems, APIs, databases, cloud services, and internal platforms.
• Design, load, and query knowledge graphs using AWS Neptune or other graph databases.
• Develop backend logic, data pipelines, and automation scripts using Python.
• Write and optimize SQL queries for data extraction, validation, transformation, and reporting.
• Apply basic machine learning concepts where required, including classification, regression, clustering, feature engineering, and model evaluation.
• Support deployment, monitoring, testing, troubleshooting, and documentation of AI automation solutions.
• Ensure AI solutions are secure, scalable, explainable, maintainable, and aligned with enterprise standards.
• Collaborate with data engineering, application, infrastructure, architecture, security, and business teams.

Mandatory Skills

• Strong hands-on experience with Large Language Models such as OpenAI, Claude, Gemini, Llama, Mistral, or similar models.
• Strong experience in prompt engineering, including system prompts, few-shot prompting, chain-of-thought-safe design, structured outputs, prompt optimization, and prompt evaluation.
• Mandatory hands-on experience creating MCP servers, MCP tools, and integrations for LLM-based applications.
• Strong programming experience in Python, including scripting, API integration, data processing, and production-quality coding practices.
• Strong working knowledge of SQL for querying, data profiling, joins, aggregations, stored procedures, and performance optimization.
• Good hands-on understanding of AWS cloud services and cloud-native deployment patterns.
• Experience with AWS Neptune or other graph databases such as Neo4j, TigerGraph, JanusGraph, or equivalent.
• Good understanding of business processes and ability to convert business requirements into AI automation use cases.
• Basic understanding of machine learning models and concepts, including supervised and unsupervised learning.
• Experience building AI agents, Retrieval-Augmented Generation pipelines, workflow automation, or tool-using LLM applications.

Preferred Technical Skills

• Experience with Retrieval-Augmented Generation using vector databases and enterprise knowledge sources.
• Experience with vector databases such as Pinecone, Weaviate, FAISS, Chroma, OpenSearch, or pgvector.
• Experience with AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
• Experience building REST APIs using FastAPI, Flask, Django REST Framework, or similar Python frameworks.
• Experience with AWS services such as Amazon Bedrock, Lambda, S3, API Gateway, CloudWatch, IAM, Step Functions, ECS, EKS, SageMaker, Glue, Athena, and OpenSearch.
• Experience with graph query languages such as Gremlin, SPARQL, or Cypher.
• Experience with data engineering concepts, ETL/ELT pipelines, batch processing, API ingestion, and data quality validation.
• Experience with Git, CI/CD, Docker, containerized deployments, and modern DevOps practices.
• Experience with LLM evaluation frameworks for answer quality, hallucination checks, safety, reliability, grounding, and accuracy.
• Knowledge of responsible AI, data privacy, access control, security, auditability, and governance practices.
• Experience with observability and monitoring for AI systems, including logging, tracing, cost tracking, latency monitoring, and usage analytics.
• Familiarity with workflow automation tools such as Airflow, Power Automate, n8n, Zapier, or similar platforms.

Required Experience

• 5+ years of software engineering, data engineering, AI engineering, automation engineering, or related technology experience.
• 2+ years of hands-on experience working with LLMs, generative AI, AI agents, or AI automation solutions.
• Proven experience building production-grade Python applications, automation scripts, APIs, or data processing workflows.
• Experience integrating AI solutions with databases, APIs, enterprise applications, and cloud services.
• Prior contractor, consulting, or project-based delivery experience is preferred.

Desired Candidate Profile

The ideal candidate should be able to work independently, understand business requirements quickly, and deliver working AI automation solutions with minimal supervision. The candidate should be comfortable working with both technical and non-technical stakeholders and should have strong analytical, communication, documentation, and problem-solving skills.

Good to Have

• Experience in enterprise AI adoption, digital transformation, or intelligent automation projects.
• Experience with chatbot, copilot, AI assistant, or agentic workflow development.
• Experience with knowledge graphs, semantic search, ontology design, and entity relationship modeling.
• Experience with document processing, OCR, summarization, information extraction, classification, and data enrichment use cases.
• Understanding of data governance, role-based access control, enterprise security, and compliance requirements.
• Exposure to regulated industries such as life sciences, healthcare, finance, manufacturing, or pharmaceuticals.
• Experience designing reusable AI components, reusable prompts, shared tool libraries, or enterprise AI platforms.

Education

• Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Information Systems, or a related field.
• Equivalent practical experience will also be considered.