AI Engineer

Responsibilities:

• Design, develop, and deploy AI/ML and generative AI solutions at scale.
• Build LLM-powered applications, including intelligent assistants, recommendation systems, search enhancement, and automation platforms.
• Develop and optimize Retrieval-Augmented Generation (RAG) architectures.
• Fine-tune and evaluate foundation models and open-source LLMs.
• Implement prompt engineering strategies to improve model performance.
• Collaborate with product managers, data scientists, and software engineers to deliver AI-driven products.
• Build scalable APIs and microservices for AI applications.
• Deploy and monitor AI workloads on AWS cloud infrastructure.
• Implement MLOps best practices for model training, deployment, monitoring, and continuous improvement.
• Ensure AI solutions meet performance, security, and reliability standards.

Requirements:

• Bachelor's or master's degree in computer science, artificial intelligence, machine learning, or a related field.
• 3-5 years of experience in software engineering, AI/ML, or data science.
• Experience building and deploying AI applications in production environments.
• Strong problem-solving and system design skills.

Technical Skills:

• Strong proficiency in Python.
• Experience with: Machine Learning, Deep Learning, Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs).
• Hands-on experience with OpenAI GPT Models, Claude, Gemini, Llama, Hugging Face, LangChain, and LlamaIndex.
• Strong understanding of RAG frameworks, vector databases (Pinecone, Weaviate, FAISS, ChromaDB), embeddings, and fine-tuning.
• Experience with AWS services such as Bedrock, SageMaker, Lambda, ECS/EKS, and S3
• Knowledge of Docker, Kubernetes, CI/CD, and MLOps.

Preferred Qualifications:

• Experience with Agentic AI and Multi-Agent Systems.
• Knowledge of recommendation engines and search technologies.
• Exposure to distributed systems and large-scale cloud architectures.
• Experience working in product-based organizations.