Senior AI Developer

Senior AI Developer - Group Bayport

Group Bayport is a rapidly growing global e-Commerce organization that has redefined the business of delivering high-quality custom products. We are seeking a highly experienced Senior AI Developer with deep expertise in Agentic AI systems to design, build, and scale autonomous AI agents capable of reasoning, planning, tool usage, multi-step execution, and real-world decision-making. This role is not limited to prompt engineering. We are looking for someone who has built production-grade, multi-agent systems that integrate LLMs, vector databases, APIs, orchestration layers, and observability frameworks. You will play a critical role in transforming our platform into an AI-First architecture by embedding intelligent agents across workflows, automation systems, and customer-facing applications.

Key Responsibilities

Agentic AI Architecture

• Design and implement autonomous AI agents capable of multi-step reasoning and task decomposition.
• Enable agents with tool usage (APIs, databases, search, calculators, external systems).
• Implement memory management (short-term + long-term) and self-reflection/corrective loops.
• Architect multi-agent collaboration systems (Planner → Executor → Critic models).
• Implement advanced reasoning frameworks like ReAct or Tree-of-Thought.

System Engineering & Integration

• Integrate LLMs (OpenAI, Anthropic, open-source models) into scalable backend systems.
• Build RAG pipelines using various vector databases (e.g., Pinecone, Weaviate).
• Develop tool invocation frameworks and function-calling pipelines.
• Optimize token usage, latency, and cost at scale.
• Implement streaming architectures for real-time AI responses.

Production-Grade Engineering

• Deploy AI systems on Kubernetes / cloud infrastructure (AWS / Azure / GCP).
• Build robust observability for prompt performance, hallucination tracking, agent failures, and token costs.
• Implement guardrails, safety layers, and compliance filters.
• Design fallback and retry strategies for system resilience.

Advanced Capabilities (Preferred)

• Fine-tuning or LoRA adaptation of LLMs.
• Building comprehensive evaluation frameworks (LLM eval pipelines, scoring agents).
• Familiarity with Reinforcement Learning from Human Feedback (RLHF).
• Experience with multi-modal AI (vision + text).
• Experience with AutoGPT, LangGraph, CrewAI, or Semantic Kernel.

Required Skills & Expertise

Core AI Stack

• Python (mandatory), strong backend fundamentals.
• Proficiency with LangChain, LangGraph, or LlamaIndex.
• Experience with agent orchestration frameworks.
• Advanced prompt engineering skills.
• Expertise in vector databases & embeddings.
• Experience with OpenAI / Anthropic APIs.
• Deep understanding of RAG architecture.

Engineering & Infrastructure

• Experience with REST / GraphQL APIs.
• Proficiency in async programming.
• Hands-on experience with Docker & Kubernetes.
• Experience with CI/CD for ML pipelines.
• Familiarity with Redis / Kafka for orchestration.
• Strong system design capability.

Architecture Knowledge

• Understanding of memory architectures (episodic, semantic).
• Knowledge of tool calling frameworks.
• Expertise in Chain-of-Thought optimization.
• Familiarity with evaluation & benchmarking strategies.
• Experience with distributed AI systems.

What We’re Looking For

• Demonstrated experience building real production AI agents, not just demos.
• Ability to think architecturally regarding cost, scale, latency, and security.
• Deep understanding of LLM limitations and mitigation strategies.
• Strong debugging ability in complex reasoning chains.
• Passion for building autonomous systems that replace manual workflows.

Nice to Have

• Experience building AI copilots.
• Experience in e-commerce, fintech, or automation platforms.
• Research background in multi-agent systems.
• Published papers or open-source contributions.
• Experience handling 1M+ AI requests/day systems.

Impact of This Role

You will architect AI agents that automate complex workflows, build multi-agent AI systems driving productivity gains, reduce operational costs through intelligent automation, and lay the foundation for an AI-First enterprise platform.

KPIs for Success

• % automation achieved through AI agents.
• Reduction in manual intervention.
• AI response latency under defined SLA.
• Token cost efficiency improvement.
• Agent accuracy and task completion rate.

Key skills/competency

• AI Development
• Agentic AI
• LLM Integration
• RAG Systems
• Python
• Vector Databases
• Kubernetes
• System Design
• Prompt Engineering
• Machine Learning