Job Summary

We are seeking an experienced Senior AI/ML Engineer with 12+ years of experience in designing, developing, deploying, and optimizing Artificial Intelligence and Machine Learning solutions across enterprise environments. The ideal candidate should have expertise in Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), MLOps, cloud platforms, and scalable AI architectures. The candidate will collaborate with cross-functional teams to build production-ready AI systems that solve complex business problems.

Key Responsibilities

• Design, develop, and deploy end-to-end AI/ML solutions for enterprise applications.
• Build scalable Machine Learning and Deep Learning models for classification, prediction, recommendation, forecasting, NLP, and computer vision use cases.
• Develop and fine-tune Large Language Models (LLMs) using prompt engineering, RAG, PEFT, LoRA, and model optimization techniques.
• Build AI-powered applications using Retrieval-Augmented Generation (RAG), AI Agents, Agentic AI workflows, and Vector Databases.
• Develop production-grade ML pipelines for model training, validation, deployment, monitoring, and retraining.
• Create scalable data pipelines for structured and unstructured datasets.
• Optimize AI models for latency, accuracy, and cost efficiency.
• Build REST APIs and AI microservices for enterprise applications.
• Implement responsible AI practices including explainability, fairness, security, governance, and compliance.
• Collaborate with Data Engineers, Data Scientists, Software Engineers, Product Managers, and DevOps teams.
• Lead AI architecture discussions and mentor junior engineers.
• Conduct model performance analysis and continuous improvement.
• Stay updated with emerging AI technologies, research papers, and industry best practices.

Required Technical Skills

Programming Languages

• Python (Expert)
• SQL
• Java (Preferred)
• Scala (Preferred)
• JavaScript (Nice to Have)

Machine Learning

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Ensemble Learning
• Time Series Forecasting
• Feature Engineering
• Model Evaluation
• Hyperparameter Tuning
• Explainable AI (XAI)

Deep Learning

• TensorFlow
• PyTorch
• Keras
• CNN
• RNN
• LSTM
• Transformers
• Attention Mechanisms

Generative AI

• OpenAI GPT Models
• Azure OpenAI
• Claude
• Gemini
• Llama
• Mistral
• Hugging Face Transformers
• Prompt Engineering
• Prompt Optimization
• Fine-Tuning
• PEFT
• LoRA
• RLHF (Preferred)

Large Language Models (LLMs)

• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• CrewAI
• AutoGen
• AI Agents
• Multi-Agent Systems
• Function Calling
• Tool Calling
• Context Management

Retrieval-Augmented Generation (RAG)

• Vector Embeddings
• Hybrid Search
• Semantic Search
• Vector Databases
• Pinecone
• Weaviate
• ChromaDB
• FAISS
• Azure AI Search
• Elasticsearch
• Knowledge Graph Integration

Natural Language Processing (NLP)

• Text Classification
• Named Entity Recognition
• Sentiment Analysis
• Summarization
• Question Answering
• Chatbots
• Conversational AI
• OCR Integration

Computer Vision

• Object Detection
• Image Classification
• Face Recognition
• OCR
• OpenCV
• YOLO
• Detectron2

Data Engineering

• Apache Spark
• PySpark
• Databricks
• Hadoop
• Kafka
• Airflow
• Delta Lake
• Snowflake
• BigQuery
• Redshift

MLOps

• MLflow
• Kubeflow
• SageMaker
• Azure ML
• Vertex AI
• Model Registry
• Feature Store
• CI/CD Pipelines
• Model Monitoring
• Drift Detection
• Experiment Tracking

Cloud Platforms

AWS

• SageMaker
• Bedrock
• Lambda
• EC2
• ECS
• EKS
• S3
• Glue
• Athena
• Redshift
• CloudWatch

Microsoft Azure

• Azure Machine Learning
• Azure OpenAI
• Azure AI Search
• Azure Data Factory
• Azure Databricks
• Azure Functions
• AKS
• Blob Storage

Google Cloud Platform

• Vertex AI
• BigQuery
• Cloud Functions
• Cloud Storage
• GKE

API Development

• FastAPI
• Flask
• Django REST Framework
• REST APIs
• GraphQL
• gRPC

DevOps

• Docker
• Kubernetes
• Helm
• Jenkins
• GitHub Actions
• Azure DevOps
• Terraform
• Ansible

Databases

• PostgreSQL
• MySQL
• SQL Server
• MongoDB
• Cassandra
• Redis

Version Control

• Git
• GitHub
• GitLab
• Bitbucket

Monitoring & Observability

• Prometheus
• Grafana
• ELK Stack
• Splunk
• Datadog

Required Qualifications

• Bachelor''s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• Master''s degree is preferred.
• 12+ years of overall IT experience with at least 6+ years in AI/ML engineering.
• Strong expertise in Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI.
• Experience deploying AI/ML solutions into production environments.
• Hands-on experience with cloud-native AI services (AWS, Azure, or Google Cloud Platform).
• Strong understanding of MLOps, model lifecycle management, and CI/CD automation.
• Experience building enterprise-grade AI platforms and scalable distributed systems.
• Excellent analytical, problem-solving, and communication skills.
• Experience leading technical teams and mentoring engineers.

Preferred Certifications

• AWS Certified Machine Learning – Specialty
• AWS Certified AI Practitioner
• Microsoft Certified: Azure AI Engineer Associate
• Microsoft Certified: Azure Data Scientist Associate
• Google Professional Machine Learning Engineer
• Databricks Machine Learning Professional
• TensorFlow Developer Certificate
• NVIDIA Deep Learning Institute Certifications