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AI - ML Engineer
We are seeking a Sr. ML Architect to join our AI/ML innovation team and drive cutting-edge artificial intelligence initiatives across diverse enterprise applications. This role combines strategic thinking with hands-on technical expertise, requiring deep knowledge in classical machine learning, deep learning, and generative AI technologies. You'll lead high-impact projects involving document intelligence, multi-agent systems, conversational AI, and automated validation workflows while working with state-of-the-art technologies to solve complex business challenges.
Responsibilities:
- Demonstrate ability to work on data science projects involving predictive modelling, NLP, computer vision, document processing, statistical analysis, vector space modelling, machine learning, and agentic AI workflows.
- Design and implement automated document validation and information extraction systems using OCR, computer vision, and NLP techniques to process physical documents and validate against existing databases.
- Develop intelligent multi-agent RAG (Retrieval-Augmented Generation) systems and advanced chatbot architectures for complex enterprise applications.
- Build AI voice agents and conversational systems to automate human validation processes and customer interactions.
- Create scalable document intelligence pipelines that extract, validate, and transform information from various document types and formats.
- Leverage rich datasets to perform research, develop models, and create AI-powered data products with the development and product teams.
- Develop novel and scalable AI systems in cooperation with system architects that enhance automation capabilities and user experiences.
- Design, develop, and deploy end-to-end machine learning solutions using classical ML algorithms, deep learning frameworks, and generative AI models for document processing and intelligent automation.
- Build and optimize large-scale ML pipelines for training, inference, and model serving in production environments across cloud platforms.
- Implement and fine-tune foundation models, including LLMs, vision models, and multimodal AI systems for document understanding and conversational applications.
- Lead experimentation with cutting-edge generative AI techniques, including prompt engineering, RAG systems, AI agents, and MCP for complex task automation and decision-making.
- Collaborate with product managers and engineering teams to identify high-value AI opportunities and translate business requirements into technical solutions.
Requirements:
- Knowledge and experience using statistical and machine learning algorithms, including regression, instance-based learning, decision trees, Bayesian statistics, clustering, neural networks, deep learning, and ensemble methods.
- Expert knowledge in Python with strong proficiency in SQL and ML frameworks (scikit-learn, TensorFlow, PyTorch).
- Should have done one or more projects involving fine-tuning with LLMs.
- Experience in feature selection, building, and optimising classifiers.
- Experience working with backend technologies such as Flask/Gunicorn, etc.
- Extensive experience with deep learning architectures (CNNs, RNNs, Transformers, GANs) and modern optimization techniques.
- Hands-on experience with generative AI technologies, including LLMs (GPT, BERT, T5), prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and MCP integration for context-aware AI applications.
- Experience designing and implementing AI agent architectures, multi-agent systems, and autonomous decision-making frameworks.
- Strong expertise in document processing, OCR, information extraction, and computer vision applications for automated validation systems.
- Experience with conversational AI, voice recognition technologies, and building intelligent chatbot systems.
- Proficiency with cloud platforms (AWS, GCP, Azure) and ML operations tools (MLflow, Kubeflow, Docker, Kubernetes).
- Strong knowledge of data preprocessing, feature engineering, and model evaluation techniques.
- Proven track record of deploying ML models in production environments with demonstrated business impact.
- Experience with A/B testing, causal inference, and experimental design methodologies.
- Knowledge of workflow automation, system integration, and API development for seamless AI solution deployment.
