Solution Architecture: Lead the design and development of scalable, secure, and high-performance architectures for Agentic AI platforms using Python and modern frameworks
Technical Standards & Deliverables: Define architecture patterns, engineering standards, and best practices for development, deployment, and system scalability
Collaboration: Work closely with product managers, engineering teams, DevOps, and QA to align technical solutions with business requirements and ensure seamless system integration
Stakeholder Interaction: Lead and execute technical POCs for Agentic AI solutions, working with customer stakeholders to define success criteria, build tailored agent configurations, and demonstrate business impact
Agent Orchestration: Architect and implement multi-agent workflows using frameworks such as LangGraph, AutoGen, or CrewAI, ensuring alignment with real-world use cases
Platform Development: Design and build resilient, scalable, multi-tenant AI platforms that support continuous innovation and production deployment
Evaluation & Benchmarking: Own the design and implementation of LLM and agent evaluation frameworks, including metrics for accuracy, hallucination, safety, and performance
Performance Optimization: Optimize system architecture and infrastructure for scalability, latency, and cost-efficiency across AI workloads
Best Practices: Establish and enforce standards across MLOps, AIOps, CI/CD, model versioning, experimentation tracking, and system observability
Leadership & Mentorship: Provide technical leadership, guide architectural decisions, and mentor engineering teams to ensure high-quality delivery
Innovation & Research: Stay updated with advancements in AI, LLMs, and agentic frameworks, continuously improving system capabilities
Documentation: Create and maintain comprehensive architectural and technical documentation
12+ years of experience in software engineering with strong expertise in Python and backend system development
Extensive experience in designing scalable, secure, multi-tenant AI/ML platforms
Deep expertise in LLMs (OpenAI, Gemini, Anthropic, Llama) and agentic AI systems
Hands-on experience with agent frameworks such as AutoGen, CrewAI, LangGraph, and LangChain ecosystem (LangChain, LangSmith, LangFlow)
Strong experience building RAG-based systems and working with vector databases
Proficiency in Python ecosystem including PyTorch, Scikit-learn, LlamaIndex, and evaluation tools like DeepEval
Deep understanding of LLM concepts (prompt engineering, fine-tuning, function/tool calling, RAG)
Strong experience in microservices architecture, REST APIs, and event-driven systems
Expertise in relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Redis)
Experience with cloud platforms (AWS, Azure, Google Cloud Platform) and cloud-native architectures
Familiarity with Docker, Kubernetes, and modern DevOps practices
Experience with CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions)
Strong exposure to observability tools for logging, monitoring, and tracing AI systems
Strong understanding of system design, scalability, and engineering best practices
Proven ability to lead architectural discussions, mentor teams, and engage with stakeholders and clients
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