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ML Engineer – Generative AI, LLMs
Ample Insight. Design, prototype, and productize scalable machine learning solutions .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in LLM engineering and machine learning, with a strong ability to prototype and productize scalable solutions. Proficient in Python ML stack and familiar with cloud platforms, showcasing analytical skills and business acumen.
Highest-signal resume keywords
LLM Engineering ExpertisePython ML Stack ProficiencyExperience with Agentic SystemsPrototyping and Production-Ready ServicesFamiliarity with AWS or Azure
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningStatisticsPrototypingLLM ProjectsAnalytical MindsetBusiness Acumen
Soft Skills
CollaborationProblem-Solving
Tools & Technologies
PythonPandasNumpyScikit-learnXGBoostPyTorchGitDockerSQLFastAPI
Certifications & Qualifications
Bachelor's Degree in Computer Science
Industry Keywords
Machine Learning SolutionsAgentic SystemsMulti-Agent CoordinationGraph DatabasesCloud Platforms
Tech Stack
Tools & technologiesAWSAzureDockerNeo4jNumpyPandasPythonPyTorchScikit-LearnSparkSQL
About the role
Key responsibilities & impact- Design, prototype, and productize scalable machine learning solutions
- Build cutting-edge systems with LLMs and real-world data
- Work closely with clients and teammates
- Turn prototypes into production-ready services
- Tackle open-ended machine learning engineering problems
Requirements
What you’ll need- Expertise in LLM engineering, including familiarity with popular LLM providers and their best practices
- Experience building or working with agentic systems (e.g. tool use, memory, planning, multi-agent coordination) is a strong plus
- LLM projects demonstrating ownership and contributions
- Track record of rapidly prototyping cutting-edge tools and research ideas and turning prototypes into production-ready services
- Hands-on experience with statistics and machine learning
- Comfortable working with the Python ML stack: Pandas, Numpy, scikit-learn, XGBoost, PyTorch, etc.
- Proficient with Git, Docker, SQL, Bash, and FastAPI
- Strong analytical mindset and business acumen
- Bachelor's degree or higher in Computer Science or a related engineering field involving coding
- Familiarity with AWS or Azure, GitHub Actions, Spark, Neo4j Cypher, and graph databases
Benefits
Comp & perks- Competitive compensation
- Remote work
- Accountability and NOT micro-management