AI/ML Engineers

Job Title: AI/ML Engineer

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Position Overview

AI/ML Engineer

Key Responsibility

AI/ML Development

• Design, train, and optimize

  machine learning models

  for real-world enterprise applications.
• Build and maintain

  end-to-end ML pipelines

  , including data pre-processing, feature engineering, model training, validation, and deployment.
• Collaborate with

  data engineers and software developers

  to integrate ML models into production systems.
• Monitor and maintain

  model performance

  , detect data drifts, and implement retraining strategies for continuous improvement.

Generative AI (GenAI)

Agentic Solution Design & Orchestration

• Architect

  LLM-powered applications

  with dynamic intent routing and tool integration.
• Develop

  agentic workflows

  using frameworks such as

  LangGraph

  or equivalents to ensure determinism, scalability, and guardrails.
• Integrate

  MCP-compatible tools and services

  to extend GenAI capabilities.

Retrieval & Embeddings

• Build and optimize

  RAG (Retrieval-Augmented Generation)

  systems using advanced embedding, chunking, and reranking strategies.
• Enhance search efficiency through

  vector store optimization

  , hybrid search, and metadata schema design.

Prompting & Model Strategy

• Design

  robust prompting frameworks

  and function-calling templates.
• Evaluate

  generic vs. fine-tuned LLMs

  based on cost, latency, accuracy, and maintainability.

Data & Integrations

• Implement

  NL2SQL

  and guarded SQL execution patterns.
• Connect models with

  microservices and enterprise APIs

  for secure, real-time data access.
• Define and maintain

  data schemas, metadata, and lineage

  for reliability and traceability.

Production Readiness

• Establish

  evaluation datasets

  and

  automated regression tests

  for RAG and agentic solutions.
• Monitor performance metrics precision, recall, hallucination rate, latency, and cost.
• Enforce

  data privacy, PII protection, and access control

  across deployments.

MLOps / LangOps

• Manage

  versioning of prompts, models, and embeddings

  , and oversee controlled rollouts with A/B testing.
• Implement

  monitoring, tracing, and telemetry

  for AI agents and tool invocations.
• Define

  fallback, timeout, and retry policies

  to ensure system reliability and resiliency.

Qualifications

• Programming:

  Strong proficiency in

  Python

  (NumPy, Pandas, Scikit-learn).

• Frameworks:

  Hands-on experience with

  TensorFlow

  or

  PyTorch

  .

• Machine Learning & Deep Learning:

  Experience with supervised, unsupervised, and reinforcement learning methods.

• Data Management:

  Proficient with

  SQL/NoSQL databases

  , data preprocessing, and feature engineering.

• MLOps:

  Exposure to

  CI/CD

  , pipeline automation, and model deployment workflows.

Preferred Skills

• Experience with

  LangChain

  ,

  LangGraph

  , or other agentic AI frameworks.
• Familiarity with

  vector databases

  and

  semantic search

  .
• Exposure to

  cloud-based ML platforms

  such as

  AWS, Azure, or GCP

  .
• Excellent

  problem-solving, communication, and collaboration

  skills.