Role & responsibilities:
- Design, develop, and deploy scalable AI/ML solutions for real-world business problems.
- Build and productionize Generative AI applications using LLMs, RAG, AI agents, embeddings, vector databases, and tool/function calling.
- Develop and fine-tune machine learning and deep learning models using frameworks such as PyTorch, TensorFlow, and Hugging Face.
- Design robust AI architectures covering data ingestion, model inference, orchestration, evaluation, monitoring, and continuous improvement.
- Build high-performance AI/ML APIs and services using Python and frameworks such as FastAPI.
- Develop and optimize RAG pipelines, including document processing, chunking, embedding, retrieval, reranking, and response generation.
- Implement LLM evaluation, observability, guardrails, hallucination mitigation, and model-quality monitoring.
- Deploy and operate AI workloads on cloud platforms such as AWS, Azure, or GCP.
- Apply MLOps and DevOps practices, including Docker, Kubernetes, CI/CD, model versioning, experiment tracking, and automated deployments.
- Optimize AI systems for latency, scalability, reliability, GPU utilization, and infrastructure cost.
- Work with data engineers, software engineers, product managers, and business stakeholders to translate requirements into production AI solutions.
- Conduct technical research and evaluate emerging AI models, frameworks, tools, and architectures.
- Establish engineering best practices around testing, security, data privacy, documentation, and code quality.
- Mentor junior and mid-level engineers and contribute to technical architecture and engineering decisions.
- Take ownership of AI projects from architecture and proof-of-concept through productio
Preferred candidate profile:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Engineering, or a related field.
- 510 years of professional software engineering, machine learning, or AI experience, with significant experience building production-grade AI systems.
- Strong proficiency in Python and solid software engineering fundamentals.
- Hands-on experience with Generative AI, LLMs, RAG, AI agents, embeddings, vector databases, and prompt engineering.
- Strong practical knowledge of PyTorch, Hugging Face, Transformers, and modern AI/ML frameworks.
- Experience with LLM fine-tuning, PEFT/LoRA, inference optimization, model evaluation, and model serving is highly desirable.
- Strong understanding of machine learning, deep learning, NLP, statistics, and model evaluation.
- Experience building and deploying production APIs and distributed services.
- Strong knowledge of Docker, Kubernetes, CI/CD, MLOps, and cloud infrastructure.
- Professional experience with at least one major cloud platform: AWS, Azure, or GCP.
- Experience with databases such as PostgreSQL, Redis, NoSQL databases, and vector databases.
- Understanding of data pipelines, ETL/ELT, distributed systems, and scalable data processing.
- Strong knowledge of software architecture, system design, testing, security, and performance optimization.
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