AI​/ML Engineer

Machine Learning Engineer

In this role, you will contribute to building and shipping AI features end-to-end: data prep, modeling, evaluation, deployment, and iteration. Additionally, collaborate with the engineering team to translate ideas and research into reliable, production-grade systems.

• Build LLM-powered features (prompt design, RAG pipelines, tools/plug-ins, evaluations, guardrails).
• Experiment with agentic AI patterns (tool use, planning/re-planning, multi-agent workflows) and ship reliable agents.
• Implement and evaluate machine-learning models (classification, regression, clustering, NLP, CV) from prototype to production.
• Write clean, well-tested Python code for data processing, modeling, and service APIs.
• Package and deploy models/services on AWS (e.g., S3, Lambda, ECS/EKS, Sage Maker) with basic CI/CD.
• Design simple, efficient data pipelines and integrate with databases (SQL/No
  
  SQL) and vector stores.
• Monitor models in production (latency, drift, quality) and iterate based on telemetry and user feedback.
• Read papers/blogs/specs and quickly translate ideas into working prototypes.

• Strong foundation in algorithms and data structures; able to analyze time/space complexity and choose the right approach.
• Solid understanding of core ML principles: bias/variance, feature engineering, cross-validation, regularization, evaluation metrics.
• Familiarity with LLMs: tokenization basics, model families, fine-tuning concepts, RAG patterns, and LLM evaluations.
• Exposure to agentic AI concepts: tool calling, planning, memory, and simple multi-agent orchestration.
• Knowledge of Model Context Protocol (MCP) for context sharing, secure integrations, and tool orchestration.
• Proficiency in Python and common libraries (Num Py, pandas, scikit-learn; plus, PyTorch or Tensor Flow preferred).
• Comfort with AWS fundamentals (IAM, S3, compute/container runtimes) or equivalent cloud experience.
• Experience with databases: writing efficient SQL, understanding normalization/indices; basic No
  
  SQL (e.g., Dynamo
  
  DB) awareness.
• Familiarity with vector databases (e.g., FAISS, Pinecone, Milvus) is a plus.
• Version control (Git) and basic software craftsmanship (testing, linting, code reviews).

• Data engineering basics:
  Airflow/Prefect, message queues, data validation.
• API development (FastAPI/Flask) and simple observability (logs/metrics/traces).
• Security, privacy, and responsible-AI awareness (PII handling, prompt injection basics, red‑team mindset).
• Math comfort: linear algebra, probability, calculus.

• Strong ability to adapt to new technologies and rapidly learn by reading docs/papers and implementing new ideas.
• Bias for action: iterate quickly, measure results, and improve based on evidence.
• Clear communication and collaborative mindset; comfortable receiving and giving feedback.

• Bachelor’s or master’s in computer science, Data Science, EE, or related field (or equivalent projects/internships).
• 0–2 years of professional experience; internships, open-source, or notable personal projects count.

Smith is an equal opportunity employer

We are an Equal Opportunity/Affirmative Action Employer.

VEVIRAA Federal Contractor