Custom Software Engineer

Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : AI Agents Workflow Integration
Good to have skills : Google CES Conversational AgentMinimum
7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: We''re looking for an Agentic AI Engineer to design, build, and deploy autonomous and semi-autonomous AI agents that can reason, plan, and execute multi-step tasks using tools, APIs, and external systems. You''ll work at the cutting edge of applied AI, building agentic workflows using frameworks like Google''s Agent Development Kit (ADK), LangChain/LangGraph, CrewAI, AutoGen, or similar, integrating them with LLMs (Gemini, Claude, GPT, open-source models) to solve real business problems.What You''ll Do Design and build agentic systems that can plan, reason, call tools, and orchestrate multi-step workflows with minimal human intervention Develop and deploy agents using frameworks such as Google ADK, LangChain/LangGraph, Semantic Kernel, CrewAI, AutoGen, or comparable agent orchestration toolkits Implement tool-calling, function-calling, and Model Context Protocol (MCP) integrations to connect agents with internal APIs, databases, and third-party services Architect memory, state management, and context-handling strategies for long-running or multi-turn agent interactions Build retrieval-augmented generation (RAG) pipelines and integrate vector databases (Pinecone, Weaviate, FAISS, Chroma, etc.) where needed Evaluate, fine-tune, and select appropriate foundation models for specific agentic use cases, balancing cost, latency, and accuracy Implement guardrails, evaluation frameworks, and observability (tracing, logging, eval harnesses) to ensure agent reliability, safety, and correctness Collaborate with product, design, and domain experts to translate business workflows into agent capabilities and task decompositions Write production-grade Python (and/or TypeScript) code, with strong testing, CI/CD, and deployment practices for agent services Stay current with the rapidly evolving agentic AI ecosystem and proactively bring in new techniques, frameworks, and best practices Document agent architectures, prompt strategies, and decision logic for maintainability and team knowledge-sharingRequired Qualifications 3+ years of software engineering experience, with at least 1+ years working directly with LLMs or generative AI systems in production Hands-on experience with at least one agent framework (Google ADK, LangChain/LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar) Strong Python skills (production-quality code, not just notebooks) experience with async programming a plus Practical understanding of prompt engineering, tool/function calling, and multi-agent orchestration patterns Experience integrating LLM APIs (OpenAI, Anthropic, Google Gemini, etc.) and working within their rate limits, token economics, and context constraints Familiarity with vector databases and RAG architecture Understanding of evaluation methodologies for non-deterministic AI systems (offline evals, human-in-the-loop review, A/B testing) Experience deploying services on cloud platforms (GCP, AWS, or Azure) using containers (Docker/Kubernetes) Solid grasp of API design, microservices, and event-driven architecturesNice to Have Experience with Model Context Protocol (MCP) server/client implementations Familiarity with Google Cloud''s Vertex AI Agent Builder or similar managed agent platforms Background in workflow orchestration tools (Airflow, Temporal, n8n) Experience with observability/tracing tools for LLM apps (LangSmith, Arize, Langfuse, Weights Biases)
Qualification 15 years full time education