Senior AI/ML Engineer

We are seeking experienced AI/ML Engineers who will be responsible for developing and driving new business opportunities internationally. The incumbent will be responsible for discovering sales opportunities and creating qualified leads. **Key Responsibilities:** - Develop ML and Deep Learning Solutions: Create predictive, classification, and optimization models using supervised, unsupervised, and reinforcement learning. - Design and Implement Generative AI Systems: Build and fine-tune Large Language Models (LLMs) and diffusion models for a range of use cases involving text, code, and multi-modal data. - Build Scalable Recommendation Engines: Develop and optimize recommendation systems using collaborative filtering, content-based filtering, hybrid models, or sequential models. - Cloud-Native ML Engineering: Deploy and manage machine learning pipelines and APIs in cloud environments (AWS, GCP, Azure),ensuring scalability, observability, and cost efficiency. - End-to-End ML Lifecycle Ownership: Own the model lifecycle from feature engineering and experimentation to deployment, CI/CD, monitoring, and iteration. - Collaborate Across Functions: Work with cross-functional teams including product managers, engineers, and data scientists to translate business goals into AI-powered solutions. **Preferred Skills:** - **4+ years of experience** in building and deploying production-level ML/DL systems - Develop proofs of concept AI/ML-based solutions and services and demonstrate them to Business stakeholders - Design and deliver ML architecture patterns operable in native and hybrid cloud architectures. - Create Functional and technical specifications for AI/ML solutions. - Implement machine learning algorithms in services and pipelines that can be used on a web scale. - Design, develop, and implement Generative AI models using state-of-the-art techniques. - Collaborate with cross-functional teams to define project goals, research requirements, and develop innovative solutions. - Strong understanding of transformer architectures and deep learning model design - Hands-on experience in building at least one production-grade Deep learning solution or Generative AI solution - Expertise in Python programming with a focus on: - ○ Code optimization and profiling - ○ Multi-threading and multiprocessing - ○ Object-oriented programming and design principles - Experience deploying models in a cloud-native environment with strong MLOps practices - Understanding of model evaluation, observability, A/B testing, and feedback loops - Excellent problem-solving and analytical skills. - Strong communication and presentation skills. Technical Stack - Languages: Excellent understanding of object-oriented concepts and Python. - ML/DL Frameworks: ○ Machine Learning: Scikit-learn, XGBoost, LightGBM ○ Deep Learning: PyTorch, TensorFlow, Keras ○ GenAI: Hugging Face Transformers, LangChain, OpenAI APIs, Gemini, Agentic Framework - Cloud & MLOps: AWS SageMaker, GCP Vertex AI, Azure ML, MLflow, Kubeflow, Docker, Kubernetes - Data & Compute: Apache Spark, BigQuery, RabbitMQ, S3, EC2 - Embedding Stores & Vector Search: FAISS, Pinecone, ChromaDB - Orchestration & APIs: Apache Airflow, Docker Deployment, and FastAPI are a must **Preferred Qualifications** - Experience with multi-modal models combining text, vision, or audio - Familiarity with Retrieval-Augmented Generation (RAG), embedding stores, and agent-based orchestration (LangGraph, ReAct) - Open-source contributions in AI/ML or GenAI ecosystems - Certifications in cloud-based machine learning platforms (AWS/GCP/Azure) **Education** - B.Tech / M.Tech / Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field - Equivalent practical experience in developing scalable AI solutions will also be considered.