AI ML Architect

Job Title:

Job Summary:

AI/ML Architect

Key Responsibilities:

• Lead the design and architecture

  of end-to-end AI/ML solutions, including training pipelines, inference engines, and deployment frameworks.
• Architect

  AI agent frameworks

  capable of autonomous decision-making, communication, and learning in complex environments.
• Develop and guide the implementation of

  Management Control Protocols (MCPs)

  to coordinate multi-agent systems and enable controllability, traceability, and resilience in AI-driven applications.
• Apply architectural patterns such as

  microservices, event-driven architecture, and serverless design

  to build scalable and modular MCP-based AI applications.
• Collaborate with data engineering teams to design robust

  data integration and management

  pipelines that ensure quality, governance, and real-time availability of structured and unstructured data.
• Define and enforce

  AI/ML architecture standards

  , best practices, and design principles across the organization.
• Lead architectural reviews and provide technical guidance to development teams during design and implementation.
• Evaluate and select appropriate tools, frameworks, and platforms for ML model development, orchestration, and serving (e.g., TensorFlow Serving, Ray, Kubeflow, MLflow).
• Ensure the designed systems are

  secure, interpretable, scalable, and monitorable

  , and integrate well with CI/CD and MLOps pipelines.
• Stay abreast of the latest advancements in

  agentic AI

  , multi-agent systems, ML system design, and relevant research trends.

Required Qualifications:

• Bachelors or Master’s degree in Computer Science, Artificial Intelligence, or a related field

• 8+ years

  of experience in software development, with at least

  5 years in AI/ML architecture or AI systems engineering.

• Proven experience designing and deploying

  agentic AI solutions

  or intelligent multi-agent systems.
• Deep understanding of

  architectural patterns

  including microservices, event-driven systems, and distributed computing.
• Experience architecting and managing

  Management Control Protocols (MCPs)

  or similar coordination frameworks in intelligent systems.
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
• Hands-on experience with containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, Azure, GCP).
• Knowledge of

  data integration strategies

  , ETL/ELT processes, and data governance.
• Familiarity with knowledge graphs, semantic systems, or AI reasoning engines is a strong plus.

Preferred Qualifications:

• Experience with agent development frameworks (e.g., LangChain, AutoGen, SPADE, JADE).
• Familiarity with real-time data processing systems (Kafka, Spark Streaming, etc.).
• Experience with reinforcement learning or decision-making systems at scale.
• Strong leadership, communication, and mentoring abilities.
• Experience in highly regulated or mission-critical domains (e.g., defense, healthcare, finance) where AI transparency and control are paramount.