Senior MLOps Technical Lead

Job Summary

Overview Senior-level role on a five-person engineering team building a production-grade healthcare conversational GenAI platform. The stack centers on Python 3.12+, FastAPI, and Google ADK 2.0 as the primary multi-agent orchestration and structured-output toolkit, deployed on Azure. The role emphasizes backend service development, GenAI workflow engineering, secure integrations, and operational ownership for patient-facing conversational experiences.Key ResponsibilitiesDesign, implement, and maintain

backend APIs and services using Python 3.12+ and FastAPI.Develop and operate

multi-agent GenAI workflows using Google ADK 2.0 as the primary orchestration framework. Integrate and tune

LLM providers and GenAI toolkits (OpenAI, Anthropic, Google). Support and extend

workflows built with other orchestration frameworks (LangGraph, LangChain, PydanticAI) and ensure interoperability.Implement RAG, prompt engineering, structured output validation, and AI guardrails

to ensure safe, reliable model behavior. Build domain task handlers

for healthcare workflows (care tasks, medications, scheduling) and integrate with clinical systems as required. Leverage Azure services

(Cosmos DB, App Configuration, Key Vault, Event Hubs, Application Insights) for data, configuration, secrets, and telemetry.Ensure API security and PHI protection

using JWT/OAuth and security best practices.Contribute to architecture, code reviews, production support, observability, and incident response. Author and maintain automated tests

(pytest) and support CI/CD and containerized deployments.Required Qualifications7+ years

professional software development experience.Strong production experience in Python and asynchronous frameworks (FastAPI preferred). Hands-on experience with Google ADK 2.0

for multi-agent orchestration and structured LLM outputs.Familiarity with other LLM orchestration frameworks (LangGraph, LangChain, PydanticAI) and ability to work across toolchains.Demonstrated knowledge of prompt engineering, RAG, structured output validation, and AI safety/guardrail patterns.Experience integrating OpenAI, Anthropic, Google, and Google ADK 2.0.Practical experience with Azure services (Cosmos DB, Key Vault, App Configuration).Familiarity with Docker, CI/CD (e.g., GitHub Actions), and containerized production deployments.Experience with event-driven architectures and workflow engines.Strong understanding of JWT/OAuth and API security best practices.Proven ability to operate with high ownership in a small, fast-paced team. Preferred Qualifications Healthcare domain experience (HIPAA, HL7/FHIR, Epic integrations).Experience with PydanticAI or equivalent structured output validation tools.Familiarity with OpenTelemetry and observability tooling.Exposure to React and TypeScript for occasional full-stack contributions.

Key Responsibilities

1. Implement and optimize ML pipelines using MLflow, Kubeflow Pipelines, and TFX, enabling automated model training, validation, and deployment.

2. Integrate DevOps practices with Python scripting to automate infrastructure provisioning via Terraform, AWS CloudFormation, and Ansible for scalable ML environments.

3. Configure and maintain CI/CD workflows using Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline code integration and deployment for ML projects.

4. Monitor and analyze ML system performance using Prometheus, Grafana, ELK Stack, and Fluentd, ensuring reliability and rapid issue resolution.

5. Apply advanced proficiency in Git, GitHub, GitLab, and Bitbucket for source code management and collaboration within the development team.

6. Participate in technical reviews, contribute to process compliance, and support feasibility studies by evaluating technical alternatives and risks for ML solutions.

7. Prepare and submit project status reports, collaborating with internal stakeholders to define deliverables and minimize escalation risks.

Skill Requirements

1. Advanced Proficiency In Ml Ops, Including Mlflow, Kubeflow Pipelines, Tfx, And Metaflow.

2. Advanced Proficiency In Devops Tools Such As Terraform, Aws Cloudformation, Ansible, Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions.

3. Advanced Proficiency In Python For Automation, Scripting, And Ml Pipeline Development.

4. Advanced Proficiency In Monitoring And Logging Tools: Prometheus, Grafana, Elk Stack, Fluentd.

5. Advanced Proficiency In Version Control Systems: Git, Github, Gitlab, Bitbucket.

6. Solid Understanding Of Cloud Infrastructure And Deployment Strategies.

7. Solid Ability To Troubleshoot, Optimize, And Maintain Ml Environments.

Other Requirements

Overview Senior-level role on a five-person engineering team building a production-grade healthcare conversational GenAI platform. The stack centers on Python 3.12+, FastAPI, and Google ADK 2.0 as the primary multi-agent orchestration and structured-output toolkit, deployed on Azure. The role emphasizes backend service development, GenAI workflow engineering, secure integrations, and operational ownership for patient-facing conversational experiences.Key ResponsibilitiesDesign, implement, and maintain

backend APIs and services using Python 3.12+ and FastAPI.Develop and operate

multi-agent GenAI workflows using Google ADK 2.0 as the primary orchestration framework. Integrate and tune

LLM providers and GenAI toolkits (OpenAI, Anthropic, Google). Support and extend

workflows built with other orchestration frameworks (LangGraph, LangChain, PydanticAI) and ensure interoperability.Implement RAG, prompt engineering, structured output validation, and AI guardrails

to ensure safe, reliable model behavior. Build domain task handlers

for healthcare workflows (care tasks, medications, scheduling) and integrate with clinical systems as required. Leverage Azure services

(Cosmos DB, App Configuration, Key Vault, Event Hubs, Application Insights) for data, configuration, secrets, and telemetry.Ensure API security and PHI protection

using JWT/OAuth and security best practices.Contribute to architecture, code reviews, production support, observability, and incident response. Author and maintain automated tests

(pytest) and support CI/CD and containerized deployments.Required Qualifications7+ years

professional software development experience.Strong production experience in Python and asynchronous frameworks (FastAPI preferred). Hands-on experience with Google ADK 2.0

for multi-agent orchestration and structured LLM outputs.Familiarity with other LLM orchestration frameworks (LangGraph, LangChain, PydanticAI) and ability to work across toolchains.Demonstrated knowledge of prompt engineering, RAG, structured output validation, and AI safety/guardrail patterns.Experience integrating OpenAI, Anthropic, Google, and Google ADK 2.0.Practical experience with Azure services (Cosmos DB, Key Vault, App Configuration).Familiarity with Docker, CI/CD (e.g., GitHub Actions), and containerized production deployments.Experience with event-driven architectures and workflow engines.Strong understanding of JWT/OAuth and API security best practices.Proven ability to operate with high ownership in a small, fast-paced team. Preferred Qualifications Healthcare domain experience (HIPAA, HL7/FHIR, Epic integrations).Experience with PydanticAI or equivalent structured output validation tools.Familiarity with OpenTelemetry and observability tooling.Exposure to React and TypeScript for occasional full-stack contributions.