Automation and AI Developer

Overview

Automation and AI Developer role focused on building and deploying AI solutions, working closely with architects, developers and business stakeholders from discovery through production.

What you'll do

• Engage internal SMEs to understand current processes and as-is workflows, and translate them into buildable AI requirements.
• Demo working solutions to internal stakeholders up to business leadership level.
• Support the Architect during feasibility and POC phases, contributing to rapid builds and validating fit.
• Maintain stakeholder communication throughout: progress updates, issue escalation and solution walkthroughs.
• Co-invest with the Architect in upfront solution design.
• Translate architecture into implementation plans and break them down into assignable streams for Developer and Associate.
• Own day-to-day technical decisions during build when the Architect is focused on other projects.
• Identify architectural risks early and escalate before they become build or production problems.
• Build complex solution components end-to-end on AWS AgentCore and LangGraph as a hands-on coding role.
• Scale solutions from POC to production, covering performance, reliability, monitoring and cost management.
• Debug production failures such as hallucination patterns, retrieval degradation, agent loops and latency regressions.
• Use AI coding tools (Claude Code, GitHub Copilot, Cursor, Cline) to accelerate delivery, and own all generated code.
• Build Low Code automations (Copilot Studio, Power Automate) when use case and bandwidth allow.
• Guide Developer and Associate through implementation, code reviews and debugging.
• Ensure the team uses AI tools effectively without sacrificing code understanding.

What you'll need

• 3–5 years total experience.
• 2–4 years of production GenAI/LLM experience.
• 12–24 months of multi-agent LangGraph experience.
• 3+ systems owned post-deployment.
• LangGraph: multi-agent state machines, parallel execution, checkpointing and HITL (non-negotiable).
• AWS Bedrock: model deployment, knowledge bases and guardrails.
• Database and AI data access: SQL proficiency, NL-to-SQL and LLM-powered query layers.
• Systems integration: REST API development, MCP awareness and enterprise system connectors.
• Advanced RAG: hybrid search, re-ranking, query reformulation and retrieval evaluation.
• Evaluation frameworks: RAGAS or TruLens, golden test sets and automated regression pipelines.
• Observability: LangSmith or LangFuse for agent tracing and production debugging.
• Python at expert level: design patterns, async, testing and CI/CD.
• Multi-model experience with OpenAI, Claude, Gemini or Llama, including trade-offs at production scale.
• Pro Code vs Low Code: working knowledge of Copilot Studio and Power Automate.
• AI development tools: Claude Code, GitHub Copilot, Cursor or Cline.
• Stakeholder communication: demo AI solutions to business stakeholders and translate SME knowledge into requirements.

Nice to have

• Snowflake experience.
• AWS AgentCore hands-on experience across all services.
• MCP / A2A server and client production implementation.
• Document intelligence: OCR and layout-aware chunking.
• Fine-tuning with LoRA / QLoRA for domain adaptation.
• Certifications: AWS ML Specialty, Azure AI Engineer or GCP ML Engineer.

Details

• Location: Hyderabad, Telangana, India.
• Work mode: Hybrid, 3 days from office.
• Shift timing: 1pm IST to 10pm IST.