Job
Description
We are seeking a proactive and technically skilled AI/ML Engineer with 2\u20133 years of experience to join our growing technology team. The ideal candidate will have hands-on expertise in AWS-based machine learning , Agentic AI , and Generative AI tools , especially within the Amazon AI ecosystem. You will play a key role in building intelligent, scalable solutions that address complex business challenges. Key Responsibilities: 1. AWS-Based Machine Learning Develop, train, and fine-tune ML models on AWS SageMaker, Bedrock, and EC2. Implement serverless ML workflows using Lambda, Step Functions, and EventBridge. Optimize models for cost/performance using AWS Inferentia/Trainium. 2. MLOps & Productionization Build CI/CD pipelines for ML using AWS SageMaker Pipelines, MLflow, or Kubeflow. Containerize models with Docker and deploy via AWS EKS/ECS/Fargate. Monitor models in production using AWS CloudWatch, SageMaker Model Monitor. 3. Agentic AI Development Design autonomous agent systems (e.g., AutoGPT, BabyAGI) for task automation. Integrate multi-agent frameworks (LangChain, AutoGen) with AWS services. Implement RAG (Retrieval-Augmented Generation) for agent knowledge enhancement. 4. Generative AI & LLMs Fine-tune and deploy LLMs (GPT-4, Claude, Llama 2/3) using LoRA/QLoRA. Build Generative AI apps (chatbots, content generators) with LangChain, LlamaIndex. Optimize prompts and evaluate LLM performance using AWS Bedrock/Amazon Titan. 5. Collaboration & Innovation Work with cross-functional teams to translate business needs into AI solutions. Collaborate with DevOps and Cloud Engineering teams to develop scalable, production-ready AI systems. Stay updated with cutting-edge AI research (arXiv, NeurIPS, ICML). 5. Governance & Documentation Implement model governance frameworks to ensure ethical AI/ML deployments. Design reproducible ML pipelines following MLOps best practices (versioning, testing, monitoring). Maintain detailed documentation for models, APIs, and workflows (Markdown, Sphinx, ReadTheDocs). Create runbooks for model deployment, troubleshooting, and scaling. Technical Skills Programming: Python (PyTorch, TensorFlow, Hugging Face Transformers). AWS: SageMaker, Lambda, ECS/EKS, Bedrock, S3, IAM. MLOps: MLflow, Kubeflow, Docker, GitHub Actions/GitLab CI. Generative AI: Prompt engineering, LLM fine-tuning, RAG, LangChain. Agentic AI: AutoGPT, BabyAGI, multi-agent orchestration. Data Engineering: SQL, PySpark, AWS Glue/EMR. Soft Skills Strong problem-solving and analytical thinking. Ability to explain complex AI concepts to non-technical stakeholders. What We\u2019re Looking For Bachelor\u2019s/Master\u2019s in CS, AI, Data Science, or related field. 2-3 years of industry experience in AI/ML engineering. Portfolio of deployed ML/AI projects (GitHub, blog, case studies). Good to have an AWS Certified Machine Learning Specialty certification. Why Join Us? Innovative Projects: Work on cutting-edge AI applications that push the boundaries of technology. Collaborative Environment: Join a team of passionate engineers and researchers committed to excellence. Career Growth: Opportunities for professional development and advancement in the rapidly evolving field of AI. Equal opportunity employer
