Key skills
PythonAIMachine LearningMLGenAILLMRAGLangChainLlamaIndexAutoGenAirflowAWSTerraformKubernetesCI/CDMentoringCommunicationCollaboration
About this role
Trajector is a company specializing in medical evidence services aimed at supporting the disabled community. They are seeking a Senior Machine Learning Engineer to design and develop scalable AI systems that enhance organizational impact, while mentoring junior engineers and collaborating with cross-functional teams.
Responsibilities:
- Design and implementation of reliable, maintainable, and scalable GenAI systems in production
- Serve as a subject matter expert for machine learning systems owned by the team
- Mentor junior and mid level engineers through code reviews, technical guidance, and design collaboration
- Design, evaluate, and implement GenAI systems and workflows for information extraction and document generation
- Ability to take a proof of concept and turn it into production capable system
- Collaborate with product managers, data scientists, and engineers to translate business needs into high quality, production ready ML solutions
- Present findings, insights, and system results to technical and non technical stakeholders
Requirements:
- Authorized to work in the United States and not require work authorization sponsorship by our company for this position now or in the future
- Bachelor's or Master's degree in Computer Science, Data Science, or related technical field
- 3-5 years of experience designing, building, and deploying scalable production applications
- Strong proficiency in Python
- Hands on experience of distributed computing and cloud platforms (g., AWS, Kubernetes, Terraform, CI/CD frameworks)
- Excellent communication and collaboration skills
- Experience with prompt engineering, RAG systems, and LLM evaluation
- Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen)
- Experience with workflow orchestration tools (e.g., Airflow, Prefect, Temporal) for managing complex ML/GenAI pipelines
- Familiarity with vector databases and retrieval-augmented generation (RAG) patterns
- Experience evaluating and mitigating LLM risks (hallucinations, bias, safety)