Lead AI/ML Engineer

Position Overview

We are seeking a highly skilled and hands-on Lead AI/ML Engineer with strong Python programming expertise to lead the design, development, implementation, and optimization of advanced Artificial Intelligence and Machine Learning solutions.

The ideal candidate will combine strong software engineering fundamentals with deep practical experience in machine learning, deep learning, and modern AI technologies. This role requires an individual who can translate complex business and technical requirements into scalable, reliable, and production-ready AI solutions while providing technical leadership, mentoring engineers, and establishing engineering best practices.

The successful candidate will work closely with cross-functional teams, including data scientists, software engineers, product stakeholders, and business teams, to deliver high-impact AI-driven applications and services.

Key Responsibilities

1. AI/ML Solution Architecture and Development

• Lead the architecture, design, development, and deployment of scalable AI/ML solutions for real-world business problems.
• Identify suitable machine learning, deep learning, and AI techniques based on business requirements and technical constraints.
• Develop robust, maintainable, and reusable AI/ML components and services.
• Evaluate emerging AI technologies, frameworks, and methodologies to improve solution performance and engineering efficiency.
• Establish technical standards, architectural patterns, and best practices for AI/ML solution development.

2. Python Engineering

• Demonstrate strong hands-on proficiency in Python for developing AI/ML models, data processing pipelines, backend services, and production applications.
• Develop clean, modular, reusable, and well-tested Python code following software engineering best practices.
• Build and integrate REST APIs and backend services to expose AI/ML capabilities to other applications.
• Apply object-oriented programming, asynchronous processing, error handling, logging, debugging, and performance optimization techniques.
• Work with relevant Python libraries and frameworks such as NumPy, pandas, scikit-learn, FastAPI, Flask, or Django, as applicable.
• Review code, improve existing implementations, and ensure the quality, reliability, and maintainability of AI-enabled applications.

3. Machine Learning and Deep Learning

• Lead the development, training, evaluation, validation, and optimization of machine learning models.
• Apply appropriate supervised, unsupervised, and other relevant learning techniques to address complex problems.
• Work with deep learning architectures and frameworks such as PyTorch and TensorFlow.
• Perform feature engineering, data preprocessing, model selection, hyperparameter tuning, and performance evaluation.
• Establish suitable evaluation metrics and validation strategies to ensure model accuracy, robustness, and generalization.
• Investigate model performance issues and implement improvements to accuracy, latency, scalability, and resource utilization.

4. Generative AI and Large Language Models

• Design and implement Generative AI solutions using Large Language Models (LLMs) and related technologies.
• Develop applications involving prompt engineering, model integration, structured outputs, and context-aware AI workflows.
• Implement Retrieval-Augmented Generation (RAG) pipelines, where relevant, including document processing, chunking, embeddings, retrieval, and response generation.
• Work with vector databases, embedding models, and semantic search technologies.
• Evaluate LLM outputs for quality, relevance, factual consistency, latency, and cost.
• Explore agent-based workflows and orchestration frameworks where appropriate to the business use case.
• Implement safeguards, validation mechanisms, and monitoring to improve the reliability and responsible use of AI-generated outputs.

5. Productionization, MLOps, and Performance Optimization

• Lead the transition of AI/ML prototypes into reliable, scalable, production-ready applications.
• Design and maintain automated model training, evaluation, deployment, and monitoring workflows.
• Apply MLOps practices covering model versioning, experiment tracking, reproducibility, deployment, and lifecycle management.
• Implement model monitoring to detect performance degradation, data drift, and other operational issues.
• Containerize applications using Docker and work with CI/CD pipelines and cloud-based deployment environments.
• Optimize inference performance, application latency, computational efficiency, and infrastructure costs.
• Establish effective testing, observability, logging, and incident troubleshooting practices for AI/ML services.

6. Technical Leadership and Mentoring

• Provide technical direction and hands-on leadership to AI/ML engineers and software development teams.
• Break down complex requirements into technical designs, implementation plans, and manageable engineering tasks.
• Conduct code reviews, architecture reviews, and technical assessments.
• Mentor engineers on Python development, AI/ML engineering, model deployment, testing, and software design principles.
• Promote engineering quality, documentation standards, knowledge sharing, and continuous improvement.
• Collaborate with stakeholders to assess feasibility, manage technical risks, and communicate progress and implementation trade-offs.

7. Data Engineering and Cross-Functional Collaboration

• Collaborate with data engineering teams to build reliable data ingestion, transformation, and preparation pipelines.
• Work with structured and unstructured data from multiple sources.
• Ensure appropriate data validation, preprocessing, quality checks, and reproducibility.
• Partner with product, business, and software engineering teams to define requirements and deliver integrated AI-powered solutions.
• Document model behavior, architecture decisions, APIs, deployment procedures, and operational considerations.

Required Qualifications and Technical Skills

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
• Significant professional experience in software engineering and AI/ML development, with demonstrated experience leading technical initiatives or engineering teams.
• Strong hands-on Python programming skills are essential.
• Strong understanding of software engineering fundamentals, data structures, algorithms, object-oriented programming, and design principles.
• Practical experience developing, training, evaluating, and deploying machine learning models.
• Experience with machine learning and data processing libraries such as scikit-learn, NumPy, and pandas.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with model evaluation, experimentation, feature engineering, and performance optimization.
• Experience building APIs, backend services, or production applications that integrate AI/ML functionality.
• Knowledge of software testing, version control, debugging, code reviews, and engineering best practices.
• Understanding of model deployment, monitoring, scalability, and production support.
• Strong problem-solving, analytical, communication, and technical leadership skills.

Preferred Qualifications and Additional Skills

• Experience with Generative AI, LLMs, prompt engineering, and RAG-based applications.
• Exposure to vector databases, embeddings, semantic search, and document-processing pipelines.
• Experience with LLM orchestration frameworks such as LangChain or LangGraph.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Experience with Docker, Kubernetes, CI/CD, and automated deployment workflows.
• Exposure to MLOps tools and practices, including experiment tracking, model registries, and model monitoring.
• Knowledge of distributed processing, data engineering frameworks, and scalable data pipelines.
• Familiarity with relational and NoSQL databases.
• Experience with model fine-tuning, quantization, inference optimization, or deployment of open-source models.
• Understanding of AI security, data privacy, responsible AI, model evaluation, and guardrails.

Key Competencies

• Strong technical ownership and hands-on engineering capability.
• Ability to lead AI/ML solution development from concept to production.
• Excellent Python coding and debugging skills.
• Strong architectural thinking and practical problem-solving ability.
• Ability to balance model performance with scalability, reliability, maintainability, and cost.
• Effective technical mentoring and cross-functional collaboration.
• Ability to communicate complex technical concepts clearly to technical and non-technical stakeholders.

What Success Looks Like

• Delivery of reliable, scalable, production-ready AI/ML solutions.
• High-quality Python code and maintainable AI/ML engineering components.
• Improved model performance, inference efficiency, and operational reliability.
• Successful integration of AI capabilities into business applications.
• Effective technical leadership, mentoring, and adherence to engineering standards.
• Continuous improvement of AI/ML development and deployment practices.

Application Process: Interested candidates are invited to submit their updated CV highlighting their Python engineering expertise, AI/ML project experience, production deployments, and technical leadership contributions.