Senior Data Engineer (AI,ML)

Essential Skills

• Proficiency in Python (Java a plus) with hands-on experience in modern ML frameworks such as PyTorch and TensorFlow, plus a solid foundation in statistics and data modeling.
• Experience building end-to-end ML and GenAI pipelines, including data preprocessing, feature engineering, model training, validation, and production deployment.
• Practical expertise in Generative AI and RAG systems, including embeddings, chunking strategies, hybrid retrieval, reranking, and evaluation techniques.
• Hands-on experience with agentic AI workflows, including prompt engineering, intent routing, tool orchestration, function calling, and safe tool-use with guardrails.
• Experience with enterprise software development and cloud-native architectures, including REST APIs, microservices, containerization, CI/CD, and platforms such as AWS, Azure, GCP, or Oracle Cloud.
• Strong problem-solving skills, with the ability to translate business requirements into scalable, reliable, and cost-effective AI solutions.
• Excellent written and verbal communication skills, with the ability to work effectively in a collaborative, cross-functional, and global team environment.

AI/ML

• Design, train, and optimize machine learning models for real-world applications.
• Build end-to-end ML pipelines, including data preprocessing, feature engineering, model training, validation, and deployment.
• Collaborate with data engineers and software developers to integrate ML models into production systems.
• Monitor model performance, detect data drift, and retrain models for continuous improvement.

GenAI

• Agentic Solution Design & Orchestration
  • Architect LLM-powered applications, including intent routing across tools and skills.
  • Implement agentic workflows using frameworks such as LangGraph or equivalents; decompose tasks, manage tool invocation, and ensure determinism and guardrails.
  • Integrate MCP-compatible tools and services to extend system capabilities.
• Retrieval & Embeddings
  • Build effective RAG systems: chunking strategies, embedding model selection, vector indexing, reranking, and grounding to authoritative data.
  • Optimize vector stores and search using ANN, hybrid retrieval, filters, and metadata schemas.
• Prompting & Model Strategy
  • Develop robust prompting patterns and templates; structure prompts for tool use and function calling.
  • Compare generic vs. fine-tuned LLMs for intent routing; make data-driven choices on cost, latency, accuracy, and maintainability.
• Data & Integrations
  • Implement NL2SQL (and guarded SQL execution) patterns; connect to microservices and enterprise systems via secure APIs.
  • Define and enforce data schemas, metadata, and lineage for reliable retrieval.
• Production Readiness
  • Establish evaluation datasets and automated regressions for RAG and agent systems.
  • Monitor quality (precision/recall, hallucination rate), latency, cost, and safety.
  • Apply guardrails, PII handling, access controls, and policy enforcement end-to-end.

MLOps / LangOps

• Version prompts, models, embeddings, and pipelines; manage A/B tests and rollout strategies.
• Instrument tracing and telemetry for agent steps and tool calls; implement fallback, timeout, and retry policies.

Core Qualifications

• Programming:
  • Strong proficiency in Python (NumPy, Pandas, Scikit-learn); experience with ML frameworks such as TensorFlow and PyTorch.
• Machine Learning & Deep Learning
  • Hands-on experience with supervised, unsupervised, and reinforcement learning techniques.
• Mathematics & Statistics
  • Solid foundation in linear algebra, probability, optimization, and statistical modeling.
• Data Handling
  • Experience with SQL and NoSQL databases, data preprocessing, and feature engineering.
• GenAI Expertise
  • Strong understanding of vector embeddings and similarity search (cosine, inner product, L2), chunking strategies, and reranking.
  • Hands-on experience building RAG pipelines (indexing, metadata, hybrid search, evaluators).
  • Practical prompt engineering for tool use, function calling, and agent planning.
  • Experience with agentic frameworks (e.g., LangGraph or similar) and orchestration of tools and services; familiarity with MCP and tool-integration patterns.
  • Knowledge of NL2SQL techniques, SQL safety (schema constraints, query sandboxes), and microservice integration.
  • Ability to evaluate tradeoffs between generic/base LLMs and fine-tuned/task-specific models (accuracy, drift, data/ops burden, latency, and cost).
  • Proficiency with Python and common LLM/RAG libraries; containerization and CI/CD.
  • Understanding of enterprise security, privacy, and compliance; RBAC/ABAC for data access, logging, and auditability.

MLOps & Deployment

• Familiarity with model deployment frameworks (MLflow, Kubeflow, SageMaker, Vertex AI), CI/CD pipelines, and containerization using Docker and Kubernetes.

Preferred Experience

• Hands-on experience with at least one major cloud provider (AWS, Azure, GCP, OCI).
• Experience with large-scale distributed systems and big data frameworks (Spark, Hadoop).
• Retrieval optimization using hybrid lexical + vector search, metadata filtering, and learned rerankers.
• Model fine-tuning and adapter methods (LoRA, SFT, DPO) and evaluation.
• Observability stacks for LLM applications (tracing, evaluation dashboards, cost/latency SLOs).
• Document AI (OCR, layout parsing) and schema construction for unstructured data.
• Caching, batching, and KV-cache optimization for throughput and cost efficiency.
• Safe tool-use patterns, including constrained decoding, JSON schemas, and policy checks.

How We’ll Assess

• Portfolio or walkthrough of a production RAG or agent system: objectives, architecture, evaluations, and outcomes.
• Hands-on exercise: design an intent router, justify model choice (generic vs. fine-tuned), propose chunking and metadata strategy, and define evaluation metrics.
• Discussion of failure modes (hallucinations, tool errors, SQL risk) and mitigation strategies.
• Approach to governance: access controls, PII handling, audit logging, and red-teaming.

Career Level - IC4