Senior AI/ ML Engineer

Job description

**Job Description**



Key responsibilities



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 drifts, and retrain models for continuous improvement.



GenAI



+ Agentic Solution design and orchestration

+ Architect LLM‑powered applications, including intent routing across tools/skills.

+ Implement agentic workflows using frameworks such as LangGraph or equivalents; decompose tasks, manage tool invocation, and ensure determinism/guardrails.

+ Integrate MCP‑compatible tools and services to extend system capabilities.

+ Retrieval and embeddings

+ Build effective RAG systems: chunking strategies, embedding model selection, vector indexing, reranking, and grounding to authoritative data.

+ Optimize vector stores and search (ANN, hybrid, filters, metadata schemas).

+ Prompting and 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 and 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 agents.

+ 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.

+ Instrument tracing/telemetry for agent steps and tool calls; implement fallback/timeout/retry policies.



Core qualifications



+ **Programming:** Strong proficiency in Python (NumPy, Pandas, Scikit-learn), experience with ML frameworks (TensorFlow, 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:**

+ Strong understanding of vector embeddings and similarity search (cosine/IP/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 orchestrating tools/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: generic/base LLMs vs fine‑tuned/task‑specific models (accuracy, drift, data/ops burden, latency/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 (Docker, 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 (hybrid lexical+vector, metadata filtering, learned rerankers).

+ Model finetuning/adapter methods (LoRA, SFT, DPO) and evaluation.

+ Observability stacks for LLM apps (tracing, eval dashboards, cost/latency SLOs).

+ Document AI (OCR, layout parsing) and schema construction for unstructured data.

+ Caching, batching, and KV‑cache considerations for throughput/cost.

+ Safe tool‑use patterns: constrained decoding, JSON schemas, policy checks.



How we’ll assess



+ Portfolio or walkthrough of a production RAG or agent system: objectives, architecture, evals, and outcomes.

+ Hands‑on exercise: design an intent router, justify model choice (generic vs fine‑tuned), propose chunking/metadata, and define eval metrics.

+ Discussion of failure modes (hallucinations, tool errors, SQL risk) and mitigations.

+ Approach to governance: access controls, PII handling, audit logging, and red‑teaming.



Career Level - IC4



**About Us**



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Required Skill Profession

Other General