Work as a core engineer building end-to-end machine learning solutions for international enterprise clients. Progress toward technical leadership by owning architecture decisions and guiding implementation. Focus on deploying robust ML pipelines using AI-augmented development practices. Engage in mentoring, knowledge sharing, and enhancing internal AI platforms
Responsibilities
- Develop and implement ML pipelines from research phase to live production systems
- Train, fine-tune, and optimize supervised, unsupervised, and generative AI models
- Produce clean, modular, and well-tested Python code with strong engineering practices
- Deploy models into production and monitor performance to detect and mitigate drift
- Build and enhance LLM-powered applications including retrieval-augmented generation (RAG) and agent-based workflows
- Integrate AI coding assistants into daily development to improve speed and code quality
- Utilize tools like Claude Code or equivalent AI coding platforms for project delivery
- Develop solutions using agent orchestration frameworks such as Bedrock AgentCore, Strands, or CrewAI
- Implement or integrate Model Context Protocol (MCP) servers for internal and client use cases
- Contribute improvements, fixes, and documentation to the internal AI toolkit
- Guide junior engineers through code reviews and provide constructive feedback
- Collaborate cross-functionally with DevOps, data engineering, and solution architecture teams
- Disseminate knowledge via documentation, presentations, or internal training sessions
- Keep current with advancements in machine learning, generative AI, and agent systems
- Suggest and implement process optimizations and reusable ML components
- Participate in high-level system design discussions and evaluate technical trade-offs
Requirements
- Strong understanding of supervised and unsupervised machine learning, including model selection, evaluation metrics, and algorithmic trade-offs
- Hands-on experience with deep learning models such as CNNs, RNNs, and Transformers, including training and fine-tuning
- Specialized expertise in at least one area: natural language processing, computer vision, recommendations, or time series forecasting
- Proven experience developing applications using large language models via OpenAI, Anthropic, or Hugging Face APIs
- Direct experience designing and implementing RAG systems, including chunking, embedding, retrieval, and generation stages
- Familiarity with vector databases including OpenSearch, Pinecone, Chroma, or FAISS
- Knowledge of prompt engineering techniques and methods for evaluating LLM outputs
- Proficiency with AI-powered coding tools such as Claude Code, Cursor, or GitHub Copilot beyond basic autocomplete
- Experience building stateful, tool-using agents using orchestration frameworks
- Understanding of Model Context Protocol (MCP), including consuming or developing MCP servers
- Ability to write technical specifications for AI systems and critically assess AI-generated code or outputs
- Awareness of monitoring, evaluation, and cost-efficiency strategies for production agent systems
- Solid experience with AWS services including SageMaker, Lambda, S3, ECR, ECS, and API Gateway
- Familiarity with Amazon Bedrock features such as model invocation, knowledge bases, and agent integration
- Basic knowledge of Infrastructure as Code using Terraform or CloudFormation
- Experience deploying ML models into production environments
- Use of experiment tracking tools like MLflow or Weights & Biases (W&B)
- Implementation of CI/CD pipelines for ML systems, including model monitoring and drift detection
- Advanced Python skills including async programming, object-oriented design, packaging, and strong use of pandas, NumPy, and SQL
- Experience containerizing ML workloads using Docker
- 1 to 3 years of practical experience in machine learning engineering
- At least one ML model successfully deployed to production or near-production
- Experience working on team-based or client-facing projects
- Demonstrated use of AI-assisted development tools in real-world workflows
- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or equivalent hands-on experience
Nice to Have
- Hold one or more AWS certifications
- Experience working with Kubernetes for orchestration and scaling
- Hands-on work with GraphRAG or custom MCP server implementations
- Contributions to open-source projects or published research in agentic systems
Tech Stack
Python, AWS, SageMaker, Lambda, S3, ECR, ECS, API Gateway, Amazon Bedrock, Terraform, CloudFormation, MLflow, Weights & Biases (W&B), Docker, OpenAI, Anthropic, Hugging Face, OpenSearch, Pinecone, Chroma, FAISS, Claude Code, Cursor, Copilot, Bedrock AgentCore
Benefits
- Competitive salary determined by skills and market benchmarks
- Access to premium AI development tools including Claude Code, Cursor, and internal AI toolkit
- Guidance and mentorship from experienced Senior ML Engineers and Tech Leads
- Defined career progression path from Mid-Level to Senior ML Engineer to Tech Lead
- Annual learning budget for courses, certifications, and industry conferences
- Remote-first work model supporting collaboration across LATAM, North America, and Europe
- Health benefits package
Compensation
Competitive salary based on competencies and market rates
Work Arrangement
global — LATAM, North America, Europe — Remote-first culture
Team
Team of 400+ engineers; collaborates with DevOps, Data Engineering, and Solutions Architects; provides mentorship to junior engineers; reports to technical leadership
- Focused on delivering production-grade AI solutions
- Embraces AI-assisted software development
- Values mentorship and knowledge transfer
- Encourages proactive problem-solving
- Promotes collaborative teamwork
- Supports continuous learning and innovation
- Committed to successful delivery in client-facing projects
Additional Information
- Fluent English proficiency (B2 level or higher) is required
- Work involves collaboration across LATAM, North America, and Europe time zones
- Fully remote work environment with distributed team operations
- Mentorship from Senior ML Engineers and Tech Leads is provided
- Clear career progression path from Mid-Level to Senior ML Engineer to Tech Lead
- Learning budget available for professional development in courses, certifications, and conferences
