Lead AI/ML Engineer
About the role
The Lead AI/ML Engineer role is responsible for leading the design, development, and optimization of advanced AI and Machine Learning solutions. The position requires strong hands-on Python programming skills and deep experience in machine learning, deep learning, and modern AI technologies. The engineer will architect and deploy scalable AI/ML solutions, provide technical leadership, mentor engineers, and establish best practices. Collaboration with cross-functional teams, including data scientists, software engineers, and business stakeholders, is essential to deliver impactful AI-driven applications. The role also involves productionizing AI/ML prototypes, implementing MLOps practices, and ensuring the reliability and scalability of deployed solutions. Candidates should have a relevant technical degree, significant professional experience in software engineering and AI/ML, and demonstrated leadership in technical initiatives or teams.
Must have
- 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.
Nice to have
- 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.
Technologies
- Ai
- Api
- AWS
- Azure
- CI/CD
- Cloud
- Data Engineering
- Data Ingestion
- Data Pipelines
- Data Science
- Deep Learning
- Django
- Docker
- Embeddings
- FastAPI
- Feature Engineering
- Fine-Tuning
- Flask
- GCP
- Generative AI
- Kubernetes
- LangChain
- LangGraph
- LLM
- Machine Learning
- MLOps
- Model Deployment
- Model Evaluation
- NoSQL
- NumPy
- Observability
- pandas
- Prompt Engineering
- Python
- PyTorch
- Quantization
- RAG
- Responsible AI
- REST
- scikit-learn
- Semantic Search
- TensorFlow
- Vector Databases
Highlights
Interested candidates should submit an updated CV that highlights Python engineering expertise, AI/ML project experience, production deployments, and technical leadership contributions. The application process is open to those with the required qualifications.
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