Senior AI/ML Engineer

Job Responsibilities

Systems Thinking & Solution Design

• Decompose business problems into technical components — identify what needs an ML model, what needs a rule, what needs a knowledge graph, and what needs a human in the loop.
• Design end-to-end system architectures for AI/ML solutions: data ingestion, feature engineering, model training, serving, monitoring, and feedback loops — with clear rationale for each design choice.
• Anticipate failure modes, bottlenecks, and degradation paths; design for observability and graceful failure from day one, not as an afterthought.

Machine Learning Engineering

• Build, train, evaluate, and tune ML models for insurance use cases such as claims severity prediction, fraud detection, risk scoring, document classification, and policy matching.
• Implement feature engineering pipelines that are reproducible, versioned, and decoupled from model training — so features can be reused across models and teams.
• Go beyond accuracy: evaluate models on business-relevant metrics (cost-of-error, false-positive impact, fairness), not just AUC and F1.

Generative AI & LLM Engineering

• Build production-grade GenAI applications using LLMs, Retrieval-Augmented Generation (RAG), tool/function calling, and Agentic AI frameworks (LangGraph, CrewAI, AutoGen).
• Design prompt and context engineering strategies grounded in domain-specific knowledge — insurance terminology, regulatory language, claims narratives.
• Build evaluation pipelines for GenAI outputs: correctness, faithfulness, relevance, latency, and cost — using frameworks such as RAGAS, LangSmith, or custom harnesses.

Knowledge Engineering & Enterprise Intelligence

• Ability to work with model domain knowledge as structured, queryable assets — ontologies, knowledge graphs, taxonomy mappings, and enriched metadata layers — that ground AI systems in the customer's business vocabulary.
• Build enterprise intelligence capabilities that combine structured data, unstructured documents, and domain rules into unified, AI-ready knowledge layers for downstream consumption by ML models and GenAI applications.
• Collaborate with domain experts to codify business rules, classification hierarchies (e.g., ISO class codes, NAICS mappings, loss-cause taxonomies), and decision logic into reusable knowledge assets.

Cloud-Native AI at Scale

Deploy and operationalize AI/ML workloads on cloud-native platforms with infrastructure that scales elastically and costs predictably.

Reference architecture on Databricks: ingest raw data via Auto Loader into Bronze Delta tables → cleanse and enrich through Silver-layer notebooks orchestrated by Databricks Workflows → compute features using Feature Store → train and log models in MLflow with experiment tracking → register production-ready models in Unity Catalog's Model Registry → serve via Model Serving endpoints behind REST APIs → monitor for drift using Lakehouse Monitoring → trigger automated retraining when quality thresholds breach. This end-to-end loop — from landing zone to live inference to feedback — is the kind of system you will own.

• Containerize models and applications using Docker; orchestrate with Kubernetes where appropriate; serve via REST APIs or event-driven architectures.
• Build and maintain MLOps/LLMOps pipelines for automated training, evaluation, deployment, monitoring, versioning, and retraining — using tools such as MLflow, Unity Catalog, Feature Store, Model Registry, and CI/CD automation (GitHub Actions, Azure DevOps, Databricks Asset Bundles).
• Implement model and data observability: drift detection, data quality checks, latency/throughput monitoring, and alerting — so the team knows when something degrades before the customer does.

Data Pipelines for AI

• Design and build data ingestion pipelines and ETL/ELT workflows using Apache Spark, Databricks, and Delta Lake.
• Build Lakehouse-based platforms; implement data quality, validation, and lineage frameworks that AI workloads depend on.
• Work with messy, heterogeneous, real-world data — inconsistent schemas, missing values, multi-format source files — and make it model-ready without losing traceability.

Business Problem Orientation

• Participate in requirement discussions and discovery sessions with business stakeholders and clients; understand the problem before reaching for a model.
• Translate business outcomes (reduce claims leakage, accelerate underwriting, improve triage accuracy) into measurable ML objectives with clear success criteria.
• Communicate results and trade-offs to non-technical stakeholders — explain what a model does, what it does not do, where it fails, and what it costs — in plain business language.

Collaboration & Reusability

• Work as part of a delivery pod alongside Forward Deployed Engineers, applied AI specialists, and consultants — own the engineering layer while contributing to end-to-end solution quality.
• Contribute reusable components — feature pipelines, evaluation harnesses, deployment templates, knowledge models, reference architectures — back to ValueMomentum's P&C accelerator library.
• Document solutions thoroughly: architecture decisions, runbooks, and operational handoff guides that let someone else maintain what you built.

Education, Technical Skills & Other Critical Requirements

Education and Experience

• 6–8 years of relevant experience in AI/ML engineering, applied AI, or enterprise intelligence delivery with demonstrated production systems.
• Bachelor's/Master's degree in Computer Science, Information Technology, Statistics, Mathematics, or equivalent practical experience.
• AI or ML Certification or Specialization on Cloud native AI architectures – E.g., Databricks ML Engineer

Programming & Engineering Fundamentals

• Strong proficiency in Python; solid software engineering practices (Git, code review, testing, modular design).
• SQL proficiency; experience with Spark (PySpark/Spark SQL).
• Building and consuming REST APIs; comfort debugging across data, model, and infrastructure layers.

Machine Learning

• Hands-on experience with supervised and unsupervised learning, ensemble methods, NLP, and deep learning fundamentals.
• Understanding of ML frameworks: scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow.
• Strong understanding of evaluation methodology — cross-validation, bias-variance trade-offs, business-metric alignment, fairness considerations.

Generative AI

• LLMs, prompt/context engineering, Retrieval-Augmented Generation (RAG), vector search and embeddings.
• Familiarity with agentic AI frameworks (LangGraph, CrewAI, or AutoGen); exposure to Model Context Protocol (MCP) is an added advantage.
• Familiarity with GenAI evaluation tooling (RAGAS, LangSmith, MLflow evaluation, or equivalent).

Knowledge Engineering & Enterprise Intelligence

• Familiarity with knowledge graphs, ontology design, or taxonomy management; experience with graph databases (e.g., Neo4j) is a plus.
• Ability to model domain knowledge as structured, queryable assets that ground AI systems in business context.

Cloud & MLOps

• Hands-on experience with at least one cloud platform: Microsoft Azure, AWS, or Databricks.
• MLOps proficiency: model deployment (batch and real-time), experiment tracking, model versioning, monitoring, and automated retraining.
• Hands-on experience with MLflow and Databricks (Workflows, Feature Store, Unity Catalog, Model Serving, Lakehouse Monitoring) is highly desirable; containerization with Docker (Kubernetes a plus).
• CI/CD for ML: GitHub Actions, Azure DevOps, or Databricks Asset Bundles.

Data Platforms

• Apache Spark, Databricks, Delta Lake, Lakehouse architecture, and data modeling.

Professional Skills

• Systems thinking: ability to reason about how components interact, where failures propagate, and what the second-order effects of a design choice are.
• Business translation: can explain a model's behavior, limitations, and value in terms a business stakeholder cares about.
• Strong analytical and problem-solving abilities; intellectual curiosity.
• Collaborative mindset; effective written and verbal communication.

Preferred Skills

• Experience in the P&C Insurance domain (Claims, Underwriting, Distribution).
• Experience building enterprise intelligence or knowledge-engineering solutions at scale.
• Unity Catalog, Databricks Asset Bundles, and Terraform or other Infrastructure-as-Code.
• Exposure to responsible AI practices: bias detection, explainability (SHAP/LIME), guardrails for GenAI.