AI Engineer

We are seeking an experienced AI Engineer to design, build, and scale advanced machine learning and generative AI systems.

This role involves bridging traditional machine learning with modern Large Language Models (LLMs), driving AI initiatives from research to production, and ensuring scalable, secure, and high-performing solutions.

• Design and architect scalable, secure, and cost-effective machine learning pipelines and generative AI applications.
• Develop and implement solutions using advanced LLM techniques, including RAG architectures, prompt engineering, and fine‑tuning approaches such as PEFT and LoRA.
• Train, evaluate, and optimize machine learning models, including predictive models, classifiers, and recommendation systems.
• Lead deployment of AI/ML models, including CI/CD processes, monitoring, and performance optimization.
• Ensure model reliability by monitoring for drift, maintaining model performance, and implementing AI safety practices.
• Collaborate with cross‑functional teams to integrate AI solutions into production systems.
• Mentor junior engineers and contribute to technical leadership within the team.

• 8–10 years of experience in Machine Learning or Software Engineering.
• Minimum 4+ years of experience deploying deep learning models into production environments.
• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Mathematics, or equivalent practical experience.
• Strong programming expertise in Python.
• Extensive experience with machine learning frameworks such as PyTorch or Tensor Flow.
• 4+ years of experience working with LLM ecosystems, including tools such as Lang Chain, Llama Index, and vector databases like Pinecone or Weaviate.
• Experience with inference optimization techniques such as vLLM or model quantization.
• Strong knowledge of traditional machine learning techniques and tools such as scikit‑learn and XGBoost.
• Experience with large-scale data processing frameworks such as Apache Spark or Ray.
• Proficiency with cloud platforms such as AWS, GCP, or Azure.
• Experience with containerization and orchestration tools such as Docker and Kubernetes.
• Familiarity with MLOps tools such as MLflow and API deployment frameworks such as FastAPI.
• Strong analytical, problem‑solving, and communication skills.

• Contributions to open‑source AI projects.
• Experience with multi‑modal AI models.
• Knowledge of AI safety, governance, and guardrails.