Senior AI/ML Engineer

ValueMomentum; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

ValueMomentum in Hyderabad is seeking an experienced Machine Learning Engineer to design and implement end-to-end AI/ML solutions for insurance use cases. You will decompose business problems, build scalable data pipelines, and create production-grade GenAI applications using LLMs, RAG, and agentic frameworks.

You will collaborate with domain experts to codify rules, evaluate models on business metrics, and implement robust MLOps, observability, and monitoring.

Qualifications

• 6-8 years of relevant experience in AI/ML engineering or enterprise intelligence delivery.
• Bachelor's or Master's degree in CS/IT/Statistics/Math or equivalent practical experience.
• AI/ML Certification on cloud-native architectures (e.g., Databricks ML Engineer).
• Strong proficiency in Python; solid software engineering practices (Git, testing).
• SQL proficiency; experience with Spark (PySpark/Spark SQL).
• Experience building REST APIs and end-to-end ML solutions.

Responsibilities

• Decompose business problems into ML, rules, graphs, and human-in-the-loop components.
• Design end-to-end AI/ML system architectures for ingestion, training, serving, and monitoring.
• Build production-grade GenAI applications using LLMs, RAG, and agentic AI frameworks.
• Develop reproducible feature pipelines and MLOps workflows with model observability.
• Collaborate with domain experts to codify rules and knowledge assets for reliability.

Skills

Python programming

REST APIs

SQL proficiency

Spark / PySpark

ML frameworks

Problem solving

Education

Bachelor's/Master's degree in CS/IT/Statistics/Math

AI/ML Certification on cloud-native architectures

Tools

Databricks

MLflow

Unity Catalog

Feature Store

Model Registry

Docker

Kubernetes

Git

PyTorch

TensorFlow

Job description

• 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.
• 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.
• 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.
• 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
• 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.
• 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).
• 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.
• 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.