Mid level AI Machine Learning Engineer  

Responsibilities:

• 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:

• 2–4 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