About UsWe are a forward-thinking organization focused on leveraging artificial intelligence and machine learning to create impactful, scalable solutions. Our projects involve cutting-edge technologies such as Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG), offering exciting opportunities to work at the forefront of innovation. Join us to help shape the future of intelligent systems! Role OverviewWe are seeking a skilled Machine Learning Engineer to design, develop, and deploy advanced AI/ML models, with a focus on Generative AI, RAG architectures, and large-scale machine learning applications. You will work on end-to-end ML pipelines, integrating state-of-the-art tools like OpenAI, Anthropic Claude, and vector databases to deliver high-quality solutions for real-world business challenges.Key Responsibilities●      Machine Learning, Generative AI & RAG Development: ●      Build and fine-tune large language models (LLMs) using frameworks such as OpenAI GPT or Anthropic Claude. ●      Design and implement RAG pipelines for scalable, real-time applications leveraging vector databases like Pinecone, Weaviate, Opensearch. ●      Develop prompt engineering strategies to optimize model outputs for specific use cases. ●      Design and deploy scalable ML models that integrate with existing systems.●      End-to-End ML Pipeline: ●      Architect, train, and deploy machine learning pipelines for NLP and multimodal AI solutions. ●      Conduct data preprocessing, feature engineering, and exploratory data analysis for training datasets. ●      Optimize embeddings for semantic search and document retrieval tasks. ●      Model Deployment & Optimization: ●      Deploy ML models in production environments using cloud platforms like AWS SageMaker, ECS or equivalent tools. ●      Ensure scalability, reliability, and low latency in production systems while monitoring model performance. ●      Implement CI/CD pipelines for ML models using Docker, Kubernetes, MLflow.●      Ensure APIs and ML services handle high traffic with minimal latency.●      Security & Compliance: ●      Ensure ML APIs follow best practices for authentication, authorization, and data privacy.●      Collaboration & Integration: ●      Work closely with cross-functional teams including data scientists, software engineers, and product managers to align ML solutions with business objectives. ●      Work with data engineers to design feature stores and streaming pipelines.●      Integrate ML outputs into enterprise systems while ensuring seamless user experiences. ●      Research & Innovation: ●      Stay updated on advancements in generative AI, LLMs, embeddings, and RAG technologies to enhance existing systems. ●      Experiment with new algorithms and frameworks to drive innovation in AI-powered applications. Required Skills & Qualifications●      Technical Expertise: ●      Proficiency in Python; familiarity with frameworks like PyTorch, TensorFlow, and libraries like Hugging Face Transformers. ●      Hands-on experience with LLMs (e.g., OpenAI GPT models, Anthropic Claude) and fine-tuning techniques. ●      Strong understanding of RAG architectures and vector database integration (e.g., Opensearch, Pinecone, Weaviate). ●      API Development: FastAPI, Flask, Django●      Containerization: Docker, AWS ECS, Kubernetes●      Cloud & Data Tools: ●      Experience with cloud platforms such as AWS (SageMaker preferred), GCP Vertex AI, or Azure ML for deploying ML models. ●      Familiarity with SQL or NoSQL databases for data extraction and preprocessing tasks. ●      Problem-Solving Skills: ●      Ability to design scalable solutions for complex problems involving unstructured data and large datasets. ●      Strong analytical skills with a focus on optimizing ML workflows for performance and efficiency. ●      Soft Skills: ●      Excellent communication skills to collaborate effectively with technical and non-technical stakeholders. ●      A passion for learning and staying ahead in the rapidly evolving field of artificial intelligence. Preferred Qualifications●      Experience building conversational AI systems or chatbots using generative AI technologies. ●      Experience with building REST API using frameworks such as Fast API.●      Experience with SQL and NoSQL database/store (Postgres, DynamoDB, Opensearch etc.)●      Knowledge of NLP techniques such as sentiment analysis, topic modeling, or summarization tasks. ●      Familiarity with serverless architectures (e.g., AWS Lambda) or ECS for scalable ML deployment. ●      Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related fields.