Machine Learning Engineer
Description
-- This is an ONSITE Role. Please apply only if you can come to office --
-- This is NOT a New College Gradate role-----------------------------------
-- Minimum of 8 years of work exp needed --------------------------------
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!
We 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.
● 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.
● Technical Expertise:
● Minimum of 8 years of work experience with hast 4 years 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.
● 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.