Lead ML/AI Platform Engineer

This is a fully remote position, open to applicants in Poland.

📋 Description

• Take full ownership of the ML/AI platform from experimentation through to production, encompassing training infrastructure, model serving, inference pipelines, and production integration.

• Collaborate with the Data Platform Architect to define the AI/ML technical direction, architecture, tools, standards, and decisions regarding build vs. buy on a company-wide scale.

• Lead the feature engineering, model training, model registry, and hosted inference efforts utilizing Amazon SageMaker.

• Promote the use of GenAI/LLM, including fine-tuning and agentic workflows within Amazon Bedrock and AgentCore.

• Construct feedback and data pipelines through AWS Glue, Lambda, and Step Functions.

• Manage the serving layer and integrate ML services with Java microservices, addressing API contracts and balancing latency/throughput considerations.

• Spearhead retrieval architectures, evaluation frameworks, serving patterns, and solution choices for GenAI/LLM projects.

• Design MCP and agent workflow patterns, stateless/stateful designs, and responsible frameworks for regulated settings.

• Collaborate with Data Scientists to productionize models and set up feature pipelines and serving infrastructure.

• Engage with product, data, and engineering leadership to pinpoint high-impact ML opportunities and convert them into actionable roadmaps.

• Represent the AI/ML function in cross-functional discussions and articulate technical trade-offs effectively.

• Work alongside DevOps to implement ML-specific CI/CD and observability enhancements.

• Define contracts and manage the serving aspect for backend Java microservices.

⛳️ Requirements

• Over 8 years of experience in software or ML engineering, including more than 5 years of delivering production ML systems.

• Proven history of managing ambiguous, high-scope challenges from start to finish.

• Demonstrated technical leadership, including developing ML strategies and mentoring senior engineers.

• Practical experience with Amazon SageMaker, Amazon Bedrock, and AgentCore.

• Familiarity with JupyterLab, Spark, and MLflow.

• In-depth knowledge of AWS S3, Athena, Redshift, Glue, Step Functions, and Lambda.

• Strong SQL skills applicable to analytical and ML tasks.

• Experience working with GenAI/LLMs, RAG, prompt engineering, and evaluation in production settings.

• Familiarity with vector databases like pgvector or Pinecone.

• Proficient in Java for reviewing service code, defining API contracts, and troubleshooting integrations.

• Extensive expertise in Python, including libraries such as scikit-learn, pandas, NumPy, PyTorch, TensorFlow, and XGBoost/LightGBM.

• Solid understanding of MLOps principles, including model monitoring, drift detection, reproducibility, experiment tracking, model registry, and cost observability.

• Exceptional written and verbal communication skills.

• Strong ability to collaborate effectively.

• Capability to function as an independent contractor through one’s own entity or an approved contracting arrangement.

• Availability to align with US Pacific business hours reliably.

• Nice to have: Experience with ClickHouse or similar columnar/real-time analytical databases.

• Nice to have: Background in fine-tuning methods such as LoRA, QLoRA, instruction tuning, or RLHF.

• Nice to have: Experience with streaming and real-time inference using Kafka or Kinesis.

• Nice to have: Proficiency in infrastructure-as-code with Terraform, AWS CDK, or CloudFormation.

• Nice to have: Experience in managing ML systems at a significant scale.

• Nice to have: Contributions to open-source projects, conference presentations, publications, or patents in the field of ML/applied ML.

🏝️ Benefits

• Equity Compensation Package.

• Flexible Time Off (FTO).

• Comprehensive Medical, Dental, and Vision coverage – with 100% of the premium paid for employees.

• Disability and Life Insurance.

• Opportunities for professional development and career advancement.

• Reimbursement for remote work setup costs.

• Monthly stipend for phone and internet expenses.

• Team-building events, cultural activities, and all-hands meetings.

• Paid time off for volunteering and community service.

• Half-day Fridays.

• 401k matching contributions.