Lead AI Engineer
- Posted 5 days ago
- Be among the first 10 applicants
Job Description
Role Overview
We are hiring a Tech Lead - AI Engineering (7+ years experience) to provide architectural oversight, define technical standards, and guide lean AI pods toward delivery excellence. You will establish baseline practices for backend infrastructure, agentic state machines, evaluation systems, and live observability. While steering overall strategy, you will remain hands-on in architecture, system reviews, and complex production problem-solving.
Key Responsibilities
- Architectural Strategy & Governance: Define and maintain production engineering standards for Python/FastAPI microservices, containerization, EKS/Kubernetes infrastructure, and API designs.
- Framework & Tooling Decisions: Guide choices between established agent frameworks (LangGraph, LangChain, Strands, ADK) and custom-built components based on engineering trade-offs.
- Production Excellence: Drive end-to-end reliability across agent loops, state management, tool execution, ground-truth evals, tracing, and production monitoring.
- Lean Pod Enablement: Mentor senior and mid-level engineers, unblock architectural challenges, and enforce high independence within 2-person pod delivery structures.
- Engineering Standards: Review code, design docs, and deployment pipelines to ensure top-tier code quality, system resilience, and production security.
Required Qualifications & Experience
- Experience: 7+ years of software engineering, backend architecture, and technical team leadership experience.
- Agentic AI Leadership: 3+ years of hands-on experience architecting and deploying production-grade agentic platforms and pipelines.
- Technical Expertise: Master-level capabilities in Python, FastAPI, Docker, EKS/Kubernetes, distributed system debugging, and API architecture.
- Lifecycle Mastery: Deep expertise in LLMOps, evaluation benchmarks (evals), ground-truth dataset creation, agent state loops, and production observability.
- Leadership Capabilities: Demonstrated success driving execution in lean engineering teams with minimal guidance and high ownership.
More Info
Key Skills
evaluation benchmarks
LLMOps
production observability
agent state loops
ground-truth dataset creation
EKS
distributed system debugging
API architecture
About Company
Skills:
Github, Functions, Data Modeling, Unit Testing, Pyspark, Nosql, Databases, Software Development Lifecycle, Docker, Python, AWS, Triggers, Sql, Numpy, Pandas, Gcp, Databricks, FastAPI, Azure, Kubernetes, embeddings, functional programming, SQL optimization, schema creation, Normalization, LLMs, cloud platforms, prompt engineering, Generative AI solutions, database architecture design, retrieval techniques
Skills:
PostgreSQL, Git, Docker, Rest Apis, Python, GenAI, LLMs, Microsoft Foundry, Azure OpenAI, Semantic Kernel, LangGraph, AI agents, Azure AI, Microsoft Agent Framework
Skills:
System Design, Application Development, Python, Testing, Databricks, Kubernetes, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems, CI/CD, Containerisation
Skills:
Kubernetes, Node.js, Java, Python, Docker, Generative AI or LLM-based solutions, DevOps practices and tools, Prompt engineering
Skills:
Java, Kafka, Spring Boot, Datadog, Redis, Postgres, Helm, Kubernetes, AWS, LangChain, LangGraph, Temporal