Digital & IT Senior Analyst - AI/ML Engineer
Indexed description
Responsibilities
Essential Functions:
- Own AI initiatives from problem framing through deployment and monitoring (data, modeling, evaluation, serving, and iteration).
- Design, train, and optimize models for NLP/LLM use cases (e.g., RAG pipelines, fine-tuning, prompt engineering, safety and guardrails).
- Build reliable ML infrastructure and services (APIs, containers, Kubernetes), integrating CI/CD and automated testing.
- Establish evaluation frameworks (offline metrics, online A/B tests, human-in-the-loop reviews) with clear success criteria.
- Implement observability for models (drift detection, performance/SLOs, error analysis, data quality checks).
- Ensure security, privacy, and compliance (PII handling, model safety, prompt-injection defenses, auditability).
- Partner with product to scope roadmaps, estimate effort, and align technical plans with business outcomes.
- Mentor engineers, contribute to architecture decisions, and champion best practices across the AI/ML stack.
- 4-year University degree
- Five or more years of experience in Information Technology
- Programming: Expert in Python and SQL; strong software engineering practices (testing, patterns, performance).
- Classical ML: supervised/unsupervised learning, model evaluation, feature engineering, time series.
- Deep Learning: PyTorch or TensorFlow, transformers, CV/NLP pipelines.
- Generative AI: LLMs, RAG, fine-tuning, prompt design, evaluation metrics and guardrails.
- Agentic AI: Practical experience with concepts such as tool-calling, reasoning loops, task planning or multi-agent orchestration (e.g., AutoGen, LangChain Agents, LangGraph)
- Data processing: Spark/Databricks or equivalent; batch and streaming (e.g., Kafka).
- Storage: relational and NoSQL; data lakes; vector databases (e.g., FAISS, Pinecone, Weaviate).
- CI/CD (e.g., GitHub Actions, GitLab CI), containerization (Docker), orchestration (Kubernetes).
- Experiment tracking and model management (e.g., MLflow, Weights & Biases, DVC).
- Cloud: Proficiency with one major cloud (AWS, GCP, or Azure) for training and serving (e.g., SageMaker, Vertex AI, AKS).
- Security and Privacy: Experience handling sensitive data (PII), encryption, access controls, secure model serving.
- Search and retrieval: Elastic/OpenSearch, knowledge graphs, advanced RAG patterns.
- Ethics and Compliance: Champions responsible AI and governance.
- Delivery: On-time, high-quality deployment of ML/LLM features into production.
- Assess current AI/ML assets, data pipelines, and platform maturity; identify quick wins and strategic gaps.
(“Minority / Female / Disability / Veteran / VEVRAA Federal Contractor”)
If you would like more information about Equal Employment Opportunity as an applicant under the law, please go to Employees & Job Applicants | U.S. Equal Employment Opportunity Commission
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