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 mustPreferred 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.