Data Science Engineer

Job Description 1: AI/ML, Python

Role Overview

The AI Engineer focuses on developing and enhancing AI capabilities under the guidance of senior engineers and architects.

Key Responsibilities

• Develop AI features using AWS Bedrock.
• Implement Python-based services and utilities.
• Build prompt flows and agent tools.
• Integrate AI services with upstream/downstream systems.
• Write clean, testable, and reusable code.
• Support unit testing and bug fixes.
• Participate in code reviews and sprint deliveries.

Key Skill Set :

• AWS Bedrock fundamentals
• AWS Neo, prompt engineering
• Strong Python fundamentals
• REST APIs and integration patterns
• Basic understanding of multi agent workflows

Job Description 2: MLOps Engineer

Machine Learning Operations (ML Ops) Engineer

Role Overview

The ML Ops Engineer is responsible for operationalizing machine learning models by building reliable, scalable, and governed pipelines that support the full ML lifecycle from development to production. The role ensures models are deployed efficiently, monitored continuously, and integrated seamlessly into data and business platforms.

Key Responsibilities

• Build and maintain end to end ML pipelines including data preparation, model training, deployment, and monitoring.
• Automate model deployment using CI/CD practices.
• Manage model versioning, experiment tracking, and reproducibility.
• Monitor model performance, data drift, and system reliability in production.
• Collaborate with data scientists and data engineers to productionize models.

Job Description 3: Gen AI Architect

Key Responsibilities

• GenAI Agentic AI Strategy
• Select and optimize opensource LLMs Llama 3 Mistral Falcon BLOOM for local deployment lead finetuning using PEFTLoRAQLoRA and full finetuning strategies
• Architect LLM distillation pipelines to create smaller domain specific models for inference efficiency
• Design and govern RAG pipelines standard RAG Graph RAG Hybrid RAG for chatbots grievance redressal helpdesk automation and document summarization
• Build reusable AI Agent frameworks task agents reasoning agents retrieval agents workflow managers using Lang Graph and Auto Gen
• Architect multiagent orchestration systems with supervisor worker patterns tool use agents and self reflection loops
• Classical ML Advanced Analytics
• Design ML pipelines for calibration models ensemble models stacking blending boosting uplift causal ML models and simulation models
• Establish XAI Explainable AI frameworks using SHAP LIME Integrated Gradients define interpretability standards for regulatory compliance
• LLM Evaluation Quality Frameworks
• Create accuracy and evaluation frameworks for summarization ROUGE BERTScore LLMasjudge extraction classification and conversational AI
• Implement hallucination detection factuality scoring and bias evaluation frameworks using LangKit GuardrailsAI and custom evaluators
• Define LLM evaluation benchmarks aligned to use cases establish continuous evaluation pipelines triggered post finetuning
• Govern prompt engineering standards fewshot template libraries and chainofthought reasoning frameworks
• Document Intelligence Knowledge Pipelines
• Build document intelligence workflows OCR Tesseract PaddleOCR parsing chunking strategies embeddings summarization extraction QA
• Design semantic search and hybrid search dense sparse architectures using vector databases PGVector Milvus Qdrant Elasticsearch
• Pipeline Automation MLOps LLMOps
• Design CICD pipelines for model retraining for LLM RAG and multiagent systems triggered automatically on data driftconcept drift metrics
• Implement automated AB testing and shadow deployment frameworks for controlled model rollouts
• Define SLAs for model inference latency throughput and availability implement performance dashboards
• Security Compliance Governance
• Ensure all model artifacts weights and dependencies are managed via a local secure repository with no external API calls
• Implement PII redaction layers data anonymization and differential privacy measures in all pipelines
• Define AI governance policies model cards data lineage audit trails and biasfairness monitoring
• Collaborate with domain teams DevOps and security teams to ensure regulatory compliance
• Design observability stack for MLLLM systems latency throughput GPU utilization drift s cost tracking
• Architect feature stores and data for reproducible ML experiments