Sr. Machine Learning Engineer

Description

• Build and deploy scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services.
• Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale.
• Stay ahead of the curve: Keep up with emerging trends, research advancements, and best practices to drive innovation and enhance our AI capabilities.
• Optimize infrastructure: Design efficient ML pipelines and leverage cloud services like AWS to ensure reliable, scalable, and cost-effective solutions.
• Evaluate generative AI applications: Help R&D teams assess and refine AI features. Build automated evaluation pipelines for model performance. Develop benchmarks to ensure accuracy, fairness, and reliability.
• Collaborate across teams: Partner with Product, Engineering, Data Engineering, and Analytics teams to align ML initiatives with business objectives and optimize for maximum impact.
• Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment.
• Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional, real-time data processing for ML and AI pipelines.
• Develop cutting-edge generative AI capabilities: Apply your expertise in LLMs and generative AI to enhance our products and build new AI features, enabling new and creative ways to interact with AI.

Requirements

• Strong familiarity with agentic frameworks for decision-making systems.
• Experience with orchestration tools (e.g. Airflow)
• Deep understanding of LLMs and generative AI, with experience applying them to solve business problems.
• Familiarity with SQL, Spark, or other data processing frameworks
• Ability to collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders.
• Hands-on expertise with Kafka and vector databases.
• You have experience working in a B2B SaaS company
• Experience with monitoring ML models using Datadog and/or OpenSearch.
• Experience with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust.
• Experience managing ML lifecycle workflows with MLflow.
• Knowledge of Snowflake or other cloud data warehouses.
• Strong proficiency in Python and popular ML Frameworks such as PyTorch, LangChain, Agents.
• Experience with AWS Cloud, Kubernetes, ArgoCD, Docker, Terraform, Jenkins and strong understanding of CI/CD pipelines for ML and model deployment best practices.
• Familiarity with Enterprise RAG Systems, including chunking, reranking techniques, etc.
• Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock.
• 4+ years of hands-on experience in building and deploying ML models in production environments.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance