Senior MLOps Technical Lead
HCLTech
Ontario, CanadaPosted about 1 month agoDiscoveredMatch locked
Full-time7+ years exp
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.
Design, 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.
Author and maintain automated tests
(pytest) and support CI/CD and containerized deployments.
7+ 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
- Implement and optimize ML pipelines using MLflow, Kubeflow Pipelines, and TFX, enabling automated model training, validation, and deployment.
- Integrate DevOps practices with Python scripting to automate infrastructure provisioning via Terraform, AWS CloudFormation, and Ansible for scalable ML environments.
- Configure and maintain CI/CD workflows using Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline code integration and deployment for ML projects.
- Monitor and analyze ML system performance using Prometheus, Grafana, ELK Stack, and Fluentd, ensuring reliability and rapid issue resolution.
- Apply advanced proficiency in Git, GitHub, GitLab, and Bitbucket for source code management and collaboration within the development team.
- Participate in technical reviews, contribute to process compliance, and support feasibility studies by evaluating technical alternatives and risks for ML solutions.
- Prepare and submit project status reports, collaborating with internal stakeholders to define deliverables and minimize escalation risks.
Skill Requirements
- Advanced Proficiency In Ml Ops, Including Mlflow, Kubeflow Pipelines, Tfx, And Metaflow.
- Advanced Proficiency In Devops Tools Such As Terraform, Aws Cloudformation, Ansible, Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions.
- Advanced Proficiency In Python For Automation, Scripting, And Ml Pipeline Development.
- Advanced Proficiency In Monitoring And Logging Tools: Prometheus, Grafana, Elk Stack, Fluentd.
- Advanced Proficiency In Version Control Systems: Git, Github, Gitlab, Bitbucket.
- Solid Understanding Of Cloud Infrastructure And Deployment Strategies.
- Solid Ability To Troubleshoot, Optimize, And Maintain Ml Environments.
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.
Design, 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.
Author and maintain automated tests
(pytest) and support CI/CD and containerized deployments.
7+ 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.
Not included in the source posting: about the role, benefits.
Skills
generative-aipythonazureci-cdfastapillmmachine-learningartificial-intelligencelangchainraggithub-actionsdocker
Who can apply
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