Senior AI Engineer

Responsibilities

• - Design and develop machine learning algorithms.

• - Design, develop, and deploy AI models and algorithms, including large language models (LLMs) and conversational agents.
  

 - Lead the development of production-ready AI/ML models and algorithms
- Optimize existing ML models for improved performance and scalability

- Collaborate with data scientists and engineers to productionize ML solutions

- Mentor junior team members and provide technical guidance

- Establish best practices for ML development and deployment

- Drive technical decision-making for AI/ML infrastructure

- Create and maintain documentation for ML systems and processes

- Evaluate and implement new ML technologies and frameworks

- Present technical findings to stakeholders and leadership

- Provide technical support for program management and business development activities including proposal writing and customer development

- Implement techniques to enhance the performance of AI systems, focusing on scalability, cost, and efficiency.

- Prepare, transform, and validate data to support AI/ML model training and deployment.

Must Have

- Proven 5+ years of hands-on AI/ML engineering experience, including direct model training, fine-tuning, and deployment.

- Bachelor’s or master’s degree in computer science, Data Science, or related field.

- Programming Languages: Python, Java, C++
- ML Frameworks: TensorFlow, PyTorch, Scikit-learn
- Cloud Platforms: AWS SageMaker, Azure ML, Google AI Platform
- Big Data: Spark, Hadoop, Kafka

- MLOps: Kubeflow, MLflow, DVC

- Databases: MongoDB, PostgreSQL, Redis
- Version Control & CI/CD: Git, Jenkins, Docker

 - Strong expertise in Python programming and ML/LLM frameworks (Hugging Face, LangChain, Scikit-learn, PyTorch, TensorFlow).

- Proven ability to build and deploy inference endpoints and APIs for AI/ML workloads.

- Proven track record of deploying AI solutions in production environments.

- Practical understanding of LLM evaluation methods (prompt testing, bias/toxicity detection, response consistency).

- Strong problem-solving skills and ability to debug real-world AI/LLM issues in production.

- Outstanding problem solving and analytical skills

- Attention to detail