Python Developer – GenAI / AI/ML Engineer

About the Role

We are seeking a Python Developer with strong backend engineering expertise and hands-on exposure to Generative AI, Machine Learning, and Deep Learning to design, build, and scale AI-driven applications.

The role involves developing production-grade AI solutions leveraging Large Language Models (LLMs), deep learning models, and cloud AI services across cloud or on-premise environments.

You will be responsible for building high-performance backend services, integrating advanced AI/ML models, and enabling scalable API-driven platforms.

The ideal candidate should have experience in building LLM-powered systems, implementing Agentic AI workflows,

and applying AI-first approaches to solve business problems.

You will work closely with cross-functional teams to deliver reliable, scalable, and secure AI solutions integrated into enterprise systems.

Key Responsibilities

• Design, develop, and integrate LLM-based solutions (e.g., OpenAI GPT, LLaMA, HuggingFace models) into enterprise products and workflows
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, chunking strategies, and fine-tuning for business use cases
• Develop APIs and integration layers to seamlessly connect AI models with frontend and backend systems
• Build and maintain scalable backend applications using Python with microservices architecture
• Design and implement RESTful APIs using frameworks such as FastAPI (mandatory), Flask, or Django
• Develop Agentic AI workflows including multi-agent coordination, tool/function calling, memory handling, and workflow orchestration
• Integrate AI models into applications using APIs and ensure secure and efficient communication across systems
• Collaborate effectively with frontend (Flutter) and backend (Node.js/Python) teams for smooth AI feature deployment
• Test, debug, and manage API integrations using tools like cURL and other debugging mechanisms
• Build and deploy AI services on cloud platforms using AWS services such as Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDS
• Leverage Amazon Bedrock and SageMaker for model deployment, orchestration, and scaling
• Develop and integrate machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, and scikit-learn
• Work on NLP, classification, regression, clustering, anomaly detection, and time-series modeling problems
• Build scalable data pipelines for data processing, training, validation, and inference
• Ensure systems are secure, scalable, cost-optimized, and production-ready with proper monitoring and observability
• Implement DevOps and MLOps best practices including CI/CD, model versioning, logging, and performance tracking
• Collaborate with product teams and stakeholders to translate business requirements into AI-driven solutions
• Contribute to architecture design, innovation, and continuous improvement of AI platforms

Required Technical Skills:

LLM & AI Integration (Mandatory Hands-on)

• Strong hands-on experience working with LLMs and Generative AI systems
• Experience integrating LLMs such as OpenAI GPT, LLaMA, HuggingFace models into real-world applications
• Experience with frameworks such as LangChain, LlamaIndex, LangGraph, ADK, or similar
• Hands-on experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or OpenSearch
• Proven ability to build and deploy RAG pipelines, embeddings-based retrieval systems, and prompt engineering workflows
• Experience integrating AI models via APIs into live production systems

Programming & Frameworks

• Strong proficiency in Python for backend development, data processing, and AI/ML integration
• Experience with FastAPI (mandatory), Flask, or Django for API development
• Basic to intermediate understanding of Node.js for backend integration and collaboration
• Basic understanding of Flutter to support frontend integration of AI APIs
• Familiarity with cURL for testing, debugging, and managing API requests and responses

Machine Learning & Deep Learning

• Solid understanding of machine learning and deep learning concepts
• Hands-on experience with frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn
• Experience in NLP, neural networks, and modern AI architectures
• Ability to train, validate, optimize, and deploy ML/DL models

Data & Database Technologies

• Experience with relational databases such as PostgreSQL or MySQL
• Experience with NoSQL and vector databases such as MongoDB, Pinecone, or OpenSearch
• Knowledge of data processing tools such as Pandas and NumPy
• Familiarity with big data tools such as Spark or Hadoop (optional)

Cloud & DevOps

• Experience working with AWS cloud services including Lambda, S3, API Gateway, EC2, ECS/EKS, DynamoDB, and RDS
• Knowledge of Amazon Bedrock and SageMaker is preferred
• Experience with Docker and Kubernetes for containerization and orchestration
• Familiarity with CI/CD pipelines and DevOps practices
• Understanding of IAM, VPC, encryption, and secure system design

Professional and Technical Skills

• Strong understanding of microservices architecture and distributed systems
• Expertise in API design, software architecture, and scalable system design
• Strong problem-solving, analytical thinking, and debugging skills
• Ability to design, build, test, deploy, and operate AI-powered systems end-to-end
• Experience in performance optimization, scalability, latency, and cost trade-offs
• Good communication skills with the ability to explain complex technical concepts to cross-functional teams
• Ability to assess existing processes, identify improvement areas, and suggest AI-driven solutions
• Awareness of latest technologies and industry trends

Good to Have

• Experience with advanced Agentic AI systems and workflow automation
• Knowledge of Graph RAG and knowledge graph-based retrieval systems
• Experience in prompt optimization, LLM fine-tuning, and model evaluation
• Experience deploying AI/ML/GenAI solutions into production environments
• Exposure to multiple cloud platforms such as AWS, Azure, or GCP
• Familiarity with financial or enterprise domain systems
• Experience with distributed systems, Snowflake, or large-scale data platforms

Summary

This role requires a strong foundation in Python backend development combined with hands-on experience in Generative AI, Machine Learning, and Deep Learning.

The candidate should be capable of building scalable, production-ready AI systems, integrating advanced models, and enabling intelligent automation across enterprise workflows.