Job Specifications
We are seeking for a Machine Learning engineer on behalf of our direct clients in USA.
Key Responsibilities
- Design, build, and maintain end-to-end MLOps pipelines for data prep, training, validation, packaging, and deployment.
- Develop FastAPI microservices for model inference with clear API contracts, versioning, and documentation.
- Define and implement deployment strategies on AKS (blue/green, canary, shadow; champion/challenger) using GitOps with Argo CD.
- Architect and evolve a self-serve MLOps platform (standards, templates, CLI/scaffolds) enabling repeatable, secure model delivery.
- Operationalize scikit-learn and other frameworks (e.g., PyTorch, XGBoost) for low-latency, scalable serving.
- Implement CI/CD for ML (test, security scan, build, package, promote) using GitHub Enterprise and related tooling.
- Integrate telemetry and observability (logging, metrics, tracing) and establish SLOs for model services.
- Monitor model and data drift; automate retraining, evaluation, and safe rollout/rollback workflows.
- Collaborate with software engineers to integrate ML services into client applications and shared platforms.
- Champion best practices for code quality, reproducibility, and governance (model registry, artifacts, approvals).
Required Qualifications
- Strong Python engineering skills and production experience building services with FastAPI.
- Proven MLOps experience: packaging, serving, scaling, and maintaining models as APIs.
- Hands-on CI/CD for ML (GitHub Enterprise or similar), including automated testing and release pipelines.
- Containerization and orchestration expertise (Docker, Kubernetes) with production deployments on AKS.
- GitOps experience with Argo CD; practical knowledge of deployment strategies (blue/green, canary, rollback).
- Solid understanding of RESTful API design, microservices patterns, and API contract governance.
- Experience designing or contributing to an MLOps platform (standards, templates, tooling) for repeatable delivery.
- Ability to work cross-functionally with data scientists, software, and platform/SRE teams.
Preferred Qualifications
- Minimum 2+ years related experience
- Experience with ML lifecycle tools (MLflow or similar for tracking/registry) and feature stores.
- Exposure to Databricks and enterprise data/compute environments.
- Cloud experience on Azure (preferred), plus GCP familiarity and managed ML services.
- Familiarity with Agile practices; experience with Helm/Kustomize, secrets management, and security scanning.
About the Company
Drishticon provides technology consulting & implementation services across retail, high tech and media industry. We focus on high volume mission critical multichannel ecommerce business functions from front end online store front to backend customer service, content, pricing and inventory management, fulfillment, accounting, payments and settlements. Our consultants bring rich industry experience and expertise and apply them to existing and new business challenges and help customers succeed.
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