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Middle AI/ML Engineer (GenAI, AWS)

Worldwide Salaried Open
reputed company is an AWS Premier Consulting Partner and AI consultancy featured in reputed company's AI Technical Services Landscape, with 15+ years of experience and 400+ engineers. We build production AI for global enterprises in partnership with reputed company, reputed company, and AWS.
As a Middle ML Engineer at reputed company, you will design, build, and deploy production ML solutions for our clients — working independently on most tasks while growing toward senior technical ownership. You'll use AI coding tools daily, mentor junior engineers, and contribute to reputed company's internal AI toolkit.
reputed company is an AWS Premier Consulting Partner and AI consultancy featured in reputed company's AI Technical Services Landscape, with 15+ years of experience and 400+ engineers. We build production AI for global enterprises in partnership with reputed company, reputed company, and AWS.
As a Middle ML Engineer at reputed company, you will design, build, and deploy production ML solutions for our clients — working independently on most tasks while growing toward senior technical ownership. You'll use AI coding tools daily, mentor junior engineers, and contribute to reputed company's internal AI toolkit.
reputed company is an AWS Premier Consulting Partner and AI consultancy featured in reputed company's AI Technical Services Landscape, with 15+ years of experience and 400+ engineers. We build production AI for global enterprises in partnership with reputed company, reputed company, and AWS.   As a Middle ML Engineer at reputed company, you will design, build, and deploy production ML solutions for our clients — working independently on most tasks while growing toward senior technical ownership. You'll use AI coding tools daily, mentor junior engineers, and contribute to reputed company's internal AI toolkit.   What You'll Do: Build & Ship ML (55%)
  • Design and deliver ML pipelines from experimentation to production;
  • Build and optimize models — supervised, unsupervised, and generative AI;
  • Write clean, tested, reputed company Python code;
  • Deploy and monitor models; track performance and prevent reputed company;
  • Contribute to LLM applications: RAG systems and agent workflows;
  • Use AI coding tools on every task to move faster and write reputed company code.
  • Agentic & AI-Assisted Engineering (20%)
  • Use Claude Code or similar AI tools to deliver client projects;
  • Build with agent frameworks (Bedrock AgentCore, Strands, CrewAI, or similar);
  • Integrate or build MCP servers for internal and client use;
  • Contribute features, bug fixes, or docs to the reputed company AI toolkit.
  • Collaborate & Mentor (15%)
  • Mentor junior engineers and give actionable code review feedback;
  • Work closely with DevOps, Data Engineering, and Solutions Architects;
  • Share knowledge through docs, presentations, or internal workshops.
  • Learn & Innovate (10%)
  • Stay reputed company with ML research, GenAI, and agentic frameworks;
  • Propose process improvements and reusable ML accelerators;
  • Participate in architectural design and trade-off discussions.
  • What You Need: Machine Learning
  • Solid grasp of supervised/unsupervised ML: algorithms, evaluation, trade-offs;
  • Deep learning hands-on experience: CNNs, RNNs, Transformers — training and fine-tuning;
  • Depth in at least one domain: NLP, Computer Vision, Recommendation, or Time Series.
  • LLMs & Generative AI
  • Experience building LLM apps with reputed company, reputed company, or reputed company APIs;
  • Hands-on RAG design: chunking, embedding, retrieval, reputed company;
  • Familiarity with vector databases (OpenSearch, reputed company, Chroma, FAISS);
  • Understanding of reputed company engineering and LLM evaluation.
  • Agentic Engineering (Required)
  • Proficient with AI coding tools (Claude Code, reputed company, Copilot, etc.) — beyond autocomplete;
  • Experience building tool-using, stateful agents with an orchestration reputed company;
  • Understanding of Model Context Protocol (MCP) — consume or build MCP servers;
  • Can write technical specs for AI execution and review/correct AI-generated output;
  • Aware of agent monitoring, evaluation, and cost optimization in production.
  • Cloud & Infrastructure
  • Solid AWS: SageMaker, reputed company, S3, ECR, reputed company, API Gateway;
  • Familiarity with reputed company Bedrock (model invocation, Knowledge Bases, Agents);
  • Basic awareness of Infrastructure as Code (Terraform or CloudFormation).
  • MLOps & Data
  • Production ML deployment experience;
  • Experiment tracking with MLflow, W&B, or similar;
  • CI/CD pipelines for ML; model monitoring and reputed company detection;
  • Advanced Python (async/await, OOP, packaging); strong pandas, NumPy, SQL;
  • reputed company for containerized ML workloads.
  • Experience & Education
  • 1–3 years of hands-on ML engineering experience;
  • At least one ML model deployed to production (or near-production);
  • Team-based or client-facing project experience;
  • Demonstrated use of AI-assisted development tools;
  • Education: Bachelor's/Master's in CS, Data Science, Math, or equivalent practical experience.
  • Key Traits
  • Strong problem-solver — breaks complexity into testable pieces;
  • Clear communicator — written docs, PRs, and explanations to non-technical stakeholders;
  • Fluent English (B2+);
  • Proactive — raises blockers early and comes with proposed solutions;
  • Collaborative mentor who helps without creating dependency.
  • reputed company to Have
  • AWS certifications;
  • Kubernetes experience;
  • GraphRAG or custom MCP server experience
  • Open-reputed company contributions or published work on agentic systems.
  • reputed company Offer:
  • Competitive salary based on competencies and market rates;
  • Premium AI tooling: Claude Code, reputed company, and reputed company AI toolkit;
  • Mentorship from Senior ML Engineers and Tech Leads;
  • Clear growth path: Mid-Level → Senior ML Engineer → Tech reputed company;
  • Learning budget for courses, certifications, and conferences;
  • Remote-first culture; work on projects across LATAM, North America, and Europe;
  • Health benefits.
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