AI/ML Developer

Position Summary

We are seeking an experienced AI/ML Developer with 11+ years of software engineering and machine learning experience to design, develop, deploy, and optimize enterprise-grade AI and machine learning solutions. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Deep Learning, Generative AI, LLMs, NLP, MLOps, and cloud platforms.

The candidate will work closely with data scientists, software engineers, product managers, and business stakeholders to develop scalable AI solutions and integrate ML models into production applications.

Key Responsibilities

• Design and develop scalable AI/ML models and intelligent applications for enterprise use cases.
• Develop machine learning solutions using Python, Scikit-learn, TensorFlow, PyTorch, and related frameworks.
• Build and deploy Generative AI and LLM-based applications, including RAG and AI-powered assistants.
• Develop NLP, classification, regression, clustering, recommendation, forecasting, and anomaly detection solutions.
• Work with Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Llama, Claude, and other foundation models.
• Implement RAG pipelines using vector databases, embeddings, semantic search, and document-processing frameworks.
• Develop prompt engineering strategies, evaluation frameworks, and model optimization techniques.
• Build and maintain ML pipelines and MLOps workflows for model training, validation, deployment, monitoring, and retraining.
• Deploy AI/ML solutions using AWS, Azure, or Google Cloud Platform cloud services.
• Develop REST APIs and microservices to integrate AI/ML models with enterprise applications.
• Optimize model performance, scalability, latency, and infrastructure cost.
• Implement model monitoring, logging, versioning, governance, and responsible AI practices.
• Collaborate with data engineers to develop data pipelines and prepare high-quality datasets for ML models.
• Conduct model experimentation, feature engineering, hyperparameter tuning, and performance evaluation.
• Mentor junior and mid-level engineers and provide technical leadership on AI/ML initiatives.
• Participate in architecture discussions, technical design, code reviews, and production support.

Required Technical Skills

Programming & Development

• Python
• SQL
• REST APIs
• FastAPI / Flask
• Microservices
• Git
• Object-Oriented Programming
• Data Structures & Algorithms

Machine Learning

• Supervised and Unsupervised Learning
• Regression & Classification
• Clustering
• Recommendation Systems
• Time-Series Forecasting
• Feature Engineering
• Model Selection & Evaluation
• Hyperparameter Optimization
• Scikit-learn

Deep Learning

• TensorFlow
• PyTorch
• Keras
• Neural Networks
• CNN
• RNN/LSTM
• Transformers
• Transfer Learning

Generative AI / LLM

• Generative AI
• Large Language Models
• OpenAI / Azure OpenAI
• Llama / Claude
• Prompt Engineering
• RAG
• Embeddings
• Vector Search
• Semantic Search
• Fine-tuning
• LangChain / LlamaIndex
• AI Agents / Agentic AI
• Function Calling / Tool Use
• LLM Evaluation

Vector Databases

• Azure AI Search
• Pinecone
• FAISS
• Weaviate
• ChromaDB
• Milvus

MLOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD
• Model Deployment
• Model Monitoring
• Feature Stores
• Model Versioning

Cloud

• AWS: SageMaker, Bedrock, S3, Lambda, EC2
• Azure: Azure Machine Learning, Azure OpenAI, Azure AI Search, Cognitive Services
• Google Cloud Platform: Vertex AI, BigQuery, Cloud Storage

Data & Big Data Technologies

• Pandas
• NumPy
• PySpark
• Apache Spark
• Databricks
• Kafka
• ETL/ELT pipelines
• Data Lakes / Data Warehouses
• Snowflake
• SQL Server / PostgreSQL / Oracle

AI Architecture

• Enterprise AI architecture
• AI/ML solution design
• Cloud-native AI applications
• Distributed ML systems
• Real-time inference
• Batch inference
• Model serving
• API-based AI integration
• Scalable data and ML pipelines