Python AIML Engineer

As an AI/ML Engineer with 4+ years of experience, your role will involve designing, developing, and deploying scalable machine learning and AI solutions to tackle real-world problems. You will utilize advanced ML, deep learning, and Generative AI techniques to ensure production-grade reliability, performance, and compliance. Key Responsibilities: - Model Development & Deployment - Design, develop, and optimize machine learning and deep learning models. - Work on supervised, unsupervised, and reinforcement learning use cases. - Deploy models into production using scalable APIs and microservices. - Optimize models for performance, latency, and cost. - Generative AI & LLMs - Build and integrate solutions using LLMs such as OpenAI and open-source models. - Implement RAG (Retrieval-Augmented Generation) pipelines. - Fine-tune models and design prompt engineering strategies. - Develop AI agents and workflow automation systems. - Data Engineering & Processing - Collect, clean, and preprocess structured and unstructured data. - Build feature engineering pipelines. - Work with large-scale datasets and distributed systems. - MLOps & Lifecycle Management - Implement CI/CD pipelines for ML models. - Monitor model performance, drift, and retraining needs. - Use tools for experiment tracking, versioning, and model governance. - Collaboration & Stakeholder Interaction - Work closely with product managers, engineers, and business stakeholders. - Translate business requirements into technical AI solutions. - Present findings and model performance to leadership. Required Skills: - Core AI/ML Skills - Strong experience in machine learning algorithms and statistical modeling. - Hands-on experience with deep learning frameworks like TensorFlow and PyTorch. - Experience with NLP, Computer Vision, or Time-Series models. - Generative AI - Experience with LLMs, prompt engineering, and RAG architectures. - Familiarity with frameworks like LangChain, LlamaIndex, or LangGraph. - Understanding of embeddings, vector databases such as FAISS and Pinecone. - Programming & Tools - Strong programming skills in Python. - Experience with libraries like scikit-learn, pandas, NumPy, and Hugging Face. - Knowledge of REST APIs, FastAPI, Flask, Docker, and Kubernetes. - Cloud & Platforms - Hands-on experience with AWS, GCP, or Azure. - Familiarity with ML services like SageMaker, Vertex AI, or Azure ML. Qualifications: - Bachelors/Masters degree in Computer Science, AI, Data Science, or related field. - Relevant certifications in AI/ML or Cloud are preferred. Preferred Experience: - Experience in BFSI, FinTech, or Healthcare domains. - Working with sensitive or regulated data environments. - Exposure to real-time AI systems and high-scale deployments. Key Competencies: - Strong problem-solving and analytical thinking. - Ability to work independently and in cross-functional teams. - Good communication and presentation skills. - Ownership mindset and attention to detail. As an AI/ML Engineer with 4+ years of experience, your role will involve designing, developing, and deploying scalable machine learning and AI solutions to tackle real-world problems. You will utilize advanced ML, deep learning, and Generative AI techniques to ensure production-grade reliability, performance, and compliance. Key Responsibilities: - Model Development & Deployment - Design, develop, and optimize machine learning and deep learning models. - Work on supervised, unsupervised, and reinforcement learning use cases. - Deploy models into production using scalable APIs and microservices. - Optimize models for performance, latency, and cost. - Generative AI & LLMs - Build and integrate solutions using LLMs such as OpenAI and open-source models. - Implement RAG (Retrieval-Augmented Generation) pipelines. - Fine-tune models and design prompt engineering strategies. - Develop AI agents and workflow automation systems. - Data Engineering & Processing - Collect, clean, and preprocess structured and unstructured data. - Build feature engineering pipelines. - Work with large-scale datasets and distributed systems. - MLOps & Lifecycle Management - Implement CI/CD pipelines for ML models. - Monitor model performance, drift, and retraining needs. - Use tools for experiment tracking, versioning, and model governance. - Collaboration & Stakeholder Interaction - Work closely with product managers, engineers, and business stakeholders. - Translate business requirements into technical AI solutions. - Present findings and model performance to leadership. Required Skills: - Core AI/ML Skills - Strong experience in machine learning algorithms and statistical modeling. - Hands-on experience with deep learning frameworks like TensorFlow and PyTorch. - Experience with NLP, Computer Vision, or Time-S