Contribute to the development and deployment of AI agents and workflow-based systems that autonomously perform reasoning and decision-making tasks.
Implement and maintain AI workflows using orchestration frameworks such as LangChain, LangGraph, or similar, enabling tool use, memory, and contextual understanding.
Integrate agents with internal and external APIs, databases, and third-party tools to enable intelligent automation and information retrieval.
Assist in the development and maintenance of API wrappers or connectors that allow agents to interact with enterprise systems and external services.
Collaborate with platform and engineering teams to modernize and document APIs, ensuring they are optimized for AI agent interoperability, observability, and security.
Support the design or implementation of Model Context Protocol (MCP) or similar standards to facilitate seamless interaction between agents and systems.
Fine-tune or adapt custom ML or foundation models for specific use cases and deploy them as part of the agentic pipeline when necessary.
Support AI-centric DevOps and MLOps workflows, including CI/CD for model services, environment configuration, versioning, and telemetry integration.
Participate in the monitoring, evaluation, and continuous improvement of deployed AI systems through feedback loops and observability metrics.
Follow responsible AI guidelines, ensuring fairness, transparency, explainability, and safety in all implementations.
Collaborate with senior engineers to document designs, improve internal AI frameworks, and maintain clean, production-ready codebases.
Requirements
Minimum Qualifications:
24 years of experience in AI engineering, software development, or intelligent systems, preferably in applied AI or workflow automation projects.
Hands-on experience building or integrating AI agents, chatbots, or intelligent workflows, ideally using frameworks such as LangChain, LangGraph, LlamaIndex, or similar.
Proficiency in Python (FastAPI) and experience working with modern software development practices (version control, testing, CI/CD).
Practical understanding of API design and integration, including REST, gRPC, or GraphQL standards.
Familiarity with machine learning concepts, model fine-tuning, embeddings, and evaluation methods.
Basic experience with LLM operations, prompt engineering, or retrieval-augmented generation (RAG) setups.
Exposure to cloud environments (Azure, AWS, GCP) and managed AI/ML services.
Understanding of DevOps/MLOps fundamentals, including CI/CD, environment automation, and telemetry.
Strong analytical and problem-solving skills, with the ability to collaborate effectively with cross-functional technical teams.
Curiosity and willingness to stay updated with emerging agentic AI, orchestration, and interoperability frameworks such as MCP or Semantic Kernel.
Preferred Qualifications
Bachelor's degree in Computer Science, Artificial Intelligence, Engineering, or a related field.
Experience deploying AI models, agents, or automation workflows into production environments.
Familiarity with vector databases (e.g., Qdrant, Pinecone, Weaviate) and retrieval-based architectures.
Knowledge of containerization (Docker), orchestration (Kubernetes), and modern CI/CD pipelines.
Interest or experience contributing to open-source AI or agentic frameworks.
Understanding of Responsible AI principles and best practices for human-in-the-loop system
