The AI/ML Developer role is designed for innovative and technically skilled professionals who are passionate about building intelligent, data-driven solutions using Machine Learning, Artificial Intelligence, and Generative AI technologies. This position focuses on designing, developing, deploying, and optimizing scalable AI-powered applications that solve complex business challenges and enhance operational efficiency across enterprise environments.
Professionals in this role will work closely with software engineers, data teams, architects, and product stakeholders to integrate advanced machine learning capabilities into modern applications and platforms. The position provides hands-on experience in machine learning model development, natural language processing, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, semantic search, vector databases, and intelligent automation frameworks.
The role requires expertise in Python development, data engineering, model training, API integration, and production deployment of AI solutions. Candidates will contribute to building enterprise-grade AI applications using modern frameworks such as LangChain, PyTorch, TensorFlow, Scikit-Learn, and cloud-based deployment environments. This opportunity is ideal for professionals seeking to advance their careers in Artificial Intelligence, Machine Learning Engineering, NLP, Generative AI, and intelligent software development while working on cutting-edge technologies and real-world innovation initiatives.
Roles & Responsibilities
- Design, develop, and deploy Machine Learning, Deep Learning, and AI-powered applications for business use cases.
- Build predictive analytics models, anomaly detection systems, recommendation engines, and semantic search solutions.
- Develop and optimize Generative AI applications using Large Language Models (LLMs) and modern AI frameworks.
- Design and implement LangChain-based workflows, intelligent agents, and multi-step AI automation pipelines.
- Build Retrieval-Augmented Generation (RAG) solutions including document processing, chunking, indexing, and context retrieval.
- Develop data processing pipelines for data collection, cleansing, transformation, feature engineering, and model preparation.
- Create scalable Python-based solutions for machine learning workflows and enterprise automation.
- Integrate AI models and intelligent services into business applications through RESTful APIs and backend services.
- Configure and manage vector databases for embedding storage, semantic search, and contextual information retrieval.
- Train, evaluate, fine-tune, and optimize machine learning models for accuracy, scalability, and performance.
- Conduct model validation, hyperparameter tuning, benchmarking, and error analysis activities.
- Monitor model performance, inference behavior, latency, and operational metrics in production environments.
- Implement observability, telemetry, and model monitoring frameworks to ensure system reliability.
- Collaborate with software development, product management, and data engineering teams to deliver AI-driven solutions.
- Participate in Agile development activities including sprint planning, stand-ups, and technical review sessions.
- Troubleshoot data pipeline issues, model failures, performance bottlenecks, and deployment challenges.
- Maintain technical documentation including architecture diagrams, API specifications, model documentation, and training records.
- Research emerging AI technologies, open-source frameworks, and Generative AI innovations to improve existing solutions.
- Contribute to continuous improvement initiatives and AI platform optimization projects.
- Ensure AI applications follow security, compliance, scalability, and performance best practices.
Key Skills
- Machine Learning, Deep Learning, and Predictive Analytics
- Artificial Intelligence, Generative AI, and Large Language Models (LLMs)
- Python Programming, Object-Oriented Design, and Data Processing
- LangChain Development, AI Agents, and Workflow Automation
- Retrieval-Augmented Generation (RAG), Prompt Engineering, and Context Management
- Natural Language Processing (NLP), Text Analytics, and Semantic Search
- PyTorch, TensorFlow, and Scikit-Learn Frameworks
- Data Engineering, Feature Engineering, and Data Pipeline Development
- Vector Databases, Embedding Models, and Similarity Search
- REST API Development, Backend Integration, and Service Architecture
- SQL Databases, NoSQL Databases, and Query Optimization
- Model Training, Hyperparameter Tuning, and Performance Optimization
- Model Monitoring, Telemetry, and AI System Observability
- Statistical Analysis, Data Interpretation, and Mathematical Modeling
- Git Version Control, Branch Management, and CI/CD Concepts
- Software Development Best Practices, Testing, and Documentation
- Cloud-Based AI Deployments, Scalability, and Infrastructure Awareness
- Problem Solving, Analytical Thinking, and Debugging Skills
- Cross-Functional Collaboration, Stakeholder Communication, and Teamwork
- Continuous Learning, Research Mindset, and Emerging Technology Adaptability
Education
- Bachelor’s Degree in Computer Science, Data Science, Information Technology, Statistics, Mathematics, Engineering, or related fields.
- Master’s Degree in Artificial Intelligence, Machine Learning, Data Science, Computer Science, or a related discipline is preferred.
- Relevant certifications in Artificial Intelligence, Machine Learning, Data Engineering, Cloud Technologies, or Data Science will be considered an added advantage.
- Candidates from diverse educational backgrounds with strong technical expertise, project experience, and demonstrated AI/ML capabilities are encouraged to apply.
