Senior Intergration AI Engineer

We're seeking a Senior AI Integration Engineer with a strong engineering foundation to design and build the data and AI infrastructure that powers our automation and business intelligence initiatives.

This role sits at the intersection of data engineering, API development, and AI, focusing on creating robust data exchange systems and multi-agent AI workflows that replace legacy processes. You'll build systems where AI agents and humans collaborate through bidirectional work delegation—both for our internal operations and the client solutions we deliver. This is a senior-level position focused on technical leadership, where you will guide architectural decisions, review pull requests, and mentor junior engineers.

The candidate will have responsibilities across the following functions:

Technical Leadership and Mentorship:

• Mentor junior engineers, providing constructive feedback and fostering technical growth.
• Conduct thorough code reviews and pull request evaluations to maintain high code quality and best practices.
• Lead the architecture and design of complex, interrelated AI and platform services.
• Champion robust design practices, including the application of design patterns and scalable and secure architecture principles.

AI Agent Integration and Automation:

• Integrate and orchestrate AI agents (LLMs, ML models, autonomous agents) into business workflows.
• Design and implement multi-agent systems, agent-to-agent communication protocols, and data handoffs.
• Build connectors and tools to enable AI agents to access enterprise data sources.
• Develop feedback loops and observability systems for AI agent performance.
• Create automated workflows that combine multiple AI capabilities (RAG systems, agent tools, multi-agent systems).

API Development and Data Exchange:

• Design, develop, and maintain scalable stateless APIs for agent communication and data exchange between systems and microservices.
• Optimise API performance, response times, token usage, and ensure high availability.

Data Infrastructure:

• Maintain and interact with data warehouses, data lakes, and vector databases.
• Build ETL/ELT pipelines that prepare data for AI consumption.
• Implement data quality checks, validation, and governance frameworks.
• Design data models that support both analytical queries and AI agent operations.

Business Intelligence Support:

• Collaborate with BI teams to expose data through APIs for dashboards and reporting.
• Build data APIs that serve real-time metrics and KPIs.
• Create data aggregation services that support analytical workloads.
• Enable self-service data access through well-documented APIs and MCP servers.

Requirements:

• 5+ years of experience in software engineering, with a strong focus on backend systems, data engineering, or AI infrastructure.
• Proven track record of leading technical projects, mentoring junior engineers, and driving architectural decisions in a collaborative environment.

Technical Expertise:

• API Development: Strong proficiency in Python and similar languages for API (REST, MCP) development.
• Database Expertise: Comfortable working with large data and pipelines. Strong knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Redis, etc. )
• Cloud Platforms: Experience with AWS, GCP, or Azure data and AI services.
• API Technologies: REST, GraphQL, gRPC, WebSockets, and message queues (Kafka, RabbitMQ, SQS, and SNS).
• Experience with AI agent frameworks (LangChain, LangGraph, LlamaIndex, Pydantic AI, AutoGen, CrewAI) and multi-agent systems.
• Understanding of LLM APIs (OpenAI, Anthropic, etc. ), prompt engineering, RAG systems, and vector databases, GraphRAG or Agentic RAG.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience with event-driven architectures and microservices.
• General understanding of data security, encryption, and compliance requirements (GDPR, HIPAA, PHI, etc. )
• General familiarity with DataOps, MLOps, or DevOps practices.
• Practical experience working with large datasets.

Leadership and Soft Skills:

• Strong problem-solving abilities and architectural thinking.
• Strong mentorship and technical coaching capabilities.
• Excellent communication skills for cross-team collaboration.
• Ability to translate business requirements into technical solutions.
• Self-motivated with the ability to work independently and in teams.
• Passion for emerging AI technologies and continuous learning.

Bonus Qualifications:

• Contributions to open-source projects related to data or AI.
• Understanding of semantic search and embedding technologies.
• Experience with workflow orchestration tools (Airflow, Temporal, Prefect, n8n).
• Prior work with business intelligence tools (Tableau, Looker, Power BI).

Category Technologies:

• Languages: Python, SQL, Rust, and Cypher.
• Data Tools: Airflow.
• APIs and Integration: FastAPI, GraphQL, Kafka, Redis, JavaScript/TypeScript, and WebAssembly (WASM).
• AI/ML: Multi-agent systems, Bedrock APIs, Pydantic AI, vector databases, RAG strategies, MCP.
• Cloud: AWS (Lambda, S3, RDS, Bedrock, and ECS) or equivalent in GCP/Azure.
• Infrastructure: Docker, Kubernetes, Terraform, GitHub, and GitHub Actions.