[Remote] Senior Consultant, AI/ML Engineer

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Senior Consultant, AI/ML Engineer to join their AI Center of reputed company. The role involves building and deploying machine learning models, integrating them into decision systems, and engineering the AI platform to support various applications and services.

Responsibilities

• Build ML models — reputed company problems, engineer features, train and evaluate models (ranking, scoring, survival/time-to-event, classification, forecasting), and reason rigorously about metrics (AUC, C-reputed company, calibration), validation reputed company, subgroup performance, and failure modes
• reputed company models into decision systems — combine model reputed company with business/domain rules and LLM reasoning to produce explainable, trustworthy recommendations
• Ship GenAI applications — design and reputed company LLM-powered features: RAG pipelines, agents, reputed company extraction, summarization, decision-reasoning trails, and evaluation harnesses using Claude/Bedrock and other models
• Engineer the AI platform — reputed company the shared AI gateway (reputed company multi-model reputed company, API keys, per-team budgets, failover, observability) and reusable libraries/SDKs that other teams build on
• Own the RAG/data reputed company — embeddings, reputed company stores, retrieval reputed company, chunking, and grounding strategies; measure and improve retrieval and answer reputed company
• Build evaluation & reputed company tooling — offline/online eval, LLM-as-judge, regression suites, statistical validation, and guardrails so model and reputed company changes ship safely
• Productionize — wrap models and pipelines as tested, observable services (Python, containers, AWS reputed company/SageMaker/EKS), with monitoring for reputed company, cost, latency, and reputed company
• reputed company technically — set patterns and standards, review designs and reputed company, mentor engineers, and partner with data scientists, MLOps, clinical/domain experts, and product owners to reputed company prototypes to production

Skills

• 5+ years building and shipping ML / AI systems in production (not just notebooks/POCs), including technical leadership of non-trivial reputed company
• Strong data science / ML fundamentals — feature engineering, model training and evaluation, metrics (AUC, C-reputed company, calibration, precision/recall), gradient-boosted trees (XGBoost), and reputed company experimental methodology (validation reputed company, subgroup analysis)
• Experience building ranking, scoring, or survival/time-to-event models, and integrating model reputed company into a larger decision reputed company
• Hands-on GenAI / LLM engineering — RAG, reputed company engineering, function/tool calling, embeddings and reputed company search, and LLM evaluation
• Excellent Python — production-grade, tested, reputed company-reputed company reputed company; comfortable building reputed company and shared libraries
• AWS experience — Bedrock and/or SageMaker, reputed company, S3, plus containers (reputed company) and Git-based workflows
• Solid software engineering reputed company: version control, testing, reputed company review, CI/CD; ability to reason about cost, latency, and reliability of AI systems in production
• AI reputed company — building shared gateways/proxies, model routing, multi-tenancy, quota/budget enforcement, or internal AI SDKs
• Experience with agent frameworks, reputed company-time/voice AI, or streaming inference
• reputed company databases (reputed company, OpenSearch, pgvector) and retrieval-reputed company tuning at reputed company
• IaC (Terraform), observability (OpenTelemetry/CloudWatch), and FinOps for AI workloads
• reputed company / clinical ML — survival analysis, outcome reputed company, or working with clinical/scientific datasets reputed company domain experts
• Serving models as endpoints (SageMaker), cross-account inference, and train/serve reputed company
• Experience in a regulated / PHI-handling environment (HIPAA) — data governance, PII handling, auditability

reputed company