Design, train, evaluate, and improve ML and DL models
Build AI solutions across domains such as NLP, computer vision, forecasting, and optimization
Design and implement GenAI solutions using API-based LLMs (e.g. RAG, agents)
Decide on the most appropriate approach (LLMs, classical ML, DL, or hybrid) based on the problem
Develop production-ready AI components and services
AI Engineering & Platform Enablement
Integrate AI models and LLM-based components into applications and workflows
Collaborate with software developers and cloud engineers on deployment and integration
Support monitoring, evaluation, and continuous improvement of AI systems
Contribute to AI architecture, best practices, and reusable components
Technical Leadership (As Team Grows)
Guide and mentor other data scientists and ML engineers
Review code, models, and solution designs
Define and enforce standards for data science, ML engineering, and GenAI development
Ensure development quality, robustness, and maintainability of AI solutions
Cross-Functional Collaboration
Work closely with business analysts, software developers, cloud engineers, and domain experts
Translate business problems into measurable AI and data solutions
Support adoption and change management for AI-driven applications
Required Skills & Experience
AI / ML Expertise
Strong experience in data science and machine learning in production environments
Deep expertise in at least one ML domain (e.g. NLP, Computer Vision, Forecasting)
Broad understanding of other ML domains and techniques
Strong Python programming skills
Experience training, evaluating, and improving ML/DL models
GenAI & Modern AI Patterns
Hands-on experience with LLM-based applications
Up-to-date knowledge of modern GenAI patterns (e.g. RAG, agents, prompt design)
Ability to assess limitations, risks, and trade-offs of GenAI approaches
Cloud & Engineering
Experience developing AI solutions on Azure
Ability and willingness to work with AWS as needed
Solid software engineering fundamentals (modularity, testing, version control)
Ability to work independently and own AI solutions end-to-end
Professional Experience
Proven experience delivering AI/ML solutions in real-world projects
Experience working in cross-functional teams
Experience in complex or enterprise environments
Nice to Have
Experience with MLOps practices and tooling
Experience deploying and monitoring ML models in production
Experience building AI-powered APIs or services
Familiarity with responsible AI, explainability, and model governance
What We Look For in This Role
A hands-on AI engineer who enjoys solving real business problems
A strong data scientist with engineering mindset
A future technical leader who can grow and guide an AI team
Someone who can balance innovation with robustness and maintainability
A pragmatic decision-maker who chooses the right AI approach for each problem
What This Role Is Not
A purely research-focused role
A PoC-only or experimentation-only position
A narrow specialization without system-level thinking
If you enjoy building production-grade AI solutions, working across ML and GenAI, and growing into a technical leadership role, this position offers the opportunity to shape how AI is delivered within AMGS AI.
