Lead Full Stack Developer - AI/ML Focus

Michael Baker International•Seattle, WA

72d•$130,000 - $170,000•Remote

About The Position

Michael Baker International is seeking a highly skilled Lead Full Stack Developer with deep AI/ML expertise to architect, build, and scale intelligent, data-driven applications across our enterprise ecosystem. This role combines strong hands-on engineering capabilities with technical leadership, guiding cross-functional teams in delivering modern, scalable, and AI-enhanced digital experiences. This role will collaborate on efforts to advance automation, middleware integration, and developer experience improvements, supporting innovation through emerging technologies for distributed AI workloads.

Requirements

Bachelor’s degree in Computer Science or related field, or similar, or equivalent experience.
Any 8+ years of full-stack engineering experience.
Expertise in JavaScript/TypeScript, Python, and modern front-end frameworks.
Strong AI/ML experience with TensorFlow, PyTorch, Scikit-Learn, or similar.
Experience deploying ML models and integrating AI features into applications.
Proficiency in microservices, distributed systems, and cloud platforms.
Strong SQL/NoSQL experience and API design skills.
Experience with enterprise-grade frameworks (.NET 8, ASP.NET Core) and cloud-native orchestration across Azure, AWS, and GCP, including Kubernetes and Helm.
Knowledge of identity and security frameworks (OAuth 2.0, OIDC).
Familiarity with MCP servers and distributed compute frameworks for AI scalability.
Data or AI/ML related certifications.

Nice To Haves

Experience with GenAI, LLMs, vector search, RAG architectures.
Experience with MLOps tools (Kubeflow, MLFlow, SageMaker).
Real-time data frameworks (Kafka, Spark).
Prior experience in regulated industries.
Open-source contributions.
Strong problem-solving and systems-thinking abilities.
Ability to lead cross-functional teams.
Excellent communication skills.
Passion for innovation and continuous learning

Responsibilities

Lead end-to-end architecture, design, and development of full-stack applications with AI/ML components.
Drive best practices in coding, scalability, security, CI/CD, and cloud-native development.
Coach and mentor developers, data engineers, and ML engineers.
Own solution design reviews, technical roadmaps, and architectural decisions.
Develop high-performance front-end interfaces using modern frameworks (React, Next.js, Angular, Vue).
Architect secure, scalable backend services using Node.js, Python, Go, or Java.
Build RESTful and GraphQL APIs.
Implement testing, code quality pipelines, and DevOps workflows.
Integrate real-time data streams for AI-driven features.
Leverage enterprise frameworks (.NET 8, ASP.NET Core) and cloud-native orchestration (Azure AKS, Kubernetes, Helm) for scalable AI deployments.
Incorporate MCP servers and distributed compute frameworks to support large-scale AI/ML inference and training.
Productionize ML models and integrate them into real-world applications.
Build ML-driven features such as recommendation engines, anomaly detection, and NLP.
Design data pipelines, feature stores, vector databases, and inference layers.
Optimize model performance and deployment strategies.
Implement model drift detection, automated retraining pipelines, and AI observability frameworks.
Research and prototype emerging AI technologies (LLMs, GenAI, RAG architectures).
Ensure responsible and ethical AI deployment practices.
Explore advanced AI applications such as digital twins and immersive analytics to accelerate innovation.
Lead cloud architecture (AWS, Azure, GCP) with serverless and containerization.
Implement CI/CD pipelines.
Ensure strong security posture aligned with Zero Trust and SOC2.
Support observability, monitoring, and data governance.
Define and execute enterprise AI/ML strategies aligned with key business outcomes.
Champion best practices in data engineering, MLOps, and cloud optimization.
Champion AI governance, compliance, and ethical AI principles across all solutions.
Promote cross-functional AI adoption and educate stakeholders on AI capabilities and limitations.
Lead and mentor AI/ML engineering teams.
Collaborate with data scientists, ML engineers, and business stakeholders to deliver impactful solutions.
Translate business requirements into scalable AI/ML strategies.