Machine Learning Engineer

About the Role

MLOps Engineer is responsible to help deploy, scale, and manage machine learning models in production environments. You will work closely with data scientists and engineering teams to automate the machine learning lifecycle, optimize model performance, and ensure smooth integration with data pipelines.

Responsibilities

• Bachelor's degree in computer science, analytics, mathematics, statistics.
• Strong experience in Python, SQL, Pyspark.
• Solid understanding and knowledge of containerization technologies (Docker, Podman, Kubernetes).
• Proficient in CI/CD pipelines, model monitoring, and MLOps platforms (e.g., AWS SageMaker, Azure ML, MLFlow).
• Proficiency in cloud platforms, specifically AWS, Azure and GCP.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Familiarity with batch processing integration for large-scale data pipelines.
• Experience with serving models using FastAPI, Flask, or similar frameworks for real-time inference.
• Certifications in AWS, Azure or ML technologies are a plus.
• Experience with Databricks is highly valued.
• Strong problem-solving and analytical skills.
• Ability to work in a team-oriented, collaborative environment.

Qualifications

• Bachelor's degree in computer science, analytics, mathematics, statistics.

Required Skills

• Model Development & Tracking: TensorFlow, PyTorch, scikit-learn, MLflow, Weights & Biases
• Model Packaging & Serving: Docker, Kubernetes, FastAPI, Flask, ONNX, TorchScript
• CI/CD & Pipelines: GitHub Actions, GitLab CI, Jenkins, ZenML, Kubeflow Pipelines, Metaflow
• Infrastructure & Orchestration: Terraform, Ansible, Apache Airflow, Prefect
• Cloud & Deployment: AWS, GCP, Azure, Serverless (Lambda, Cloud Functions)
• Monitoring & Logging: Prometheus, Grafana, ELK Stack, WhyLabs, Evidently AI, Arize
• Testing & Validation: Pytest, unittest, Pydantic, Great Expectations
• Feature Store & Data Handling: Feast, Tecton, Hopsworks, Pandas, Spark, Dask
• Message Brokers & Data Streams: Kafka, Redis Streams
• Vector DB & LLM Integrations (optional): Pinecone, FAISS, Weaviate, LangChain, LlamaIndex, PromptLayer.

Preferred Skills

• Experience with Databricks is highly valued.
• Certifications in AWS, Azure or ML technologies are a plus.