Note: The job is a remote job and is open to candidates in USA. EazyML is recognized by Gartner and specializes in Responsible AI, focusing on proactive compliance and sustainable automation for enterprises. They are seeking a Senior GenAI / Agentic AI Engineer with deep expertise in designing, building, and deploying scalable Generative AI and LLM-based systems, playing a key role in solution architecture and client interactions.
Responsibilities
- Architect and build scalable Generative AI systems and agentic AI applications, from prototyping through production deployment
- Design and implement LLM-powered workflows, including prompt engineering for reflexive, self-learning, and multi-agent systems
- Assemble intelligent AI agents using frameworks such as LangChain and LangGraph to address a range of enterprise use cases (e.g., natural language to SQL translation, autonomous task agents, retrieval-augmented workflows)
- Guide the selection, customization, fine-tuning, and optimization of state-of-the-art LLMs and generative AI models
- Design end-to-end machine learning and GenAI pipelines covering training, deployment, monitoring, and lifecycle management in production
- Build APIs, microservices, and integration frameworks to embed AI capabilities into enterprise applications
- Define best practices for responsible AI development, including strategies to mitigate hallucinations, bias, and reliability risks
- Collaborate with product, engineering, and business teams — and directly with customers — to define technical requirements and translate them into robust AI solution architectures
- Ensure AI platforms meet enterprise standards for performance, reliability, security, scalability, and data governance/privacy
- Provide technical leadership and mentorship to engineering teams, while contributing to the long-term AI platform and technology strategy
Skills
- Minimum 2+ years of hands-on experience in Generative AI and 8+ years overall in traditional Machine Learning, including the creation, training, and deployment of models such as recommendation engines, deep learning models, and generative AI systems
- Strong hands-on experience with LLMs (GPT and similar), prompt engineering, and building reflexive/self-learning agentic systems
- Practical experience with agentic AI frameworks such as LangChain and LangGraph, and a track record of assembling intelligent agents for real use cases (e.g., an NLQ-to-SQL translator or comparable agentic application)
- Strong Python skills — building API wrappers, integrating with third-party APIs, and developing internal utilities and tooling
- Solid grounding in modern neural network architectures: Transformers, CNNs, and RNNs, along with hands-on experience with TensorFlow, PyTorch, and Scikit-learn
- Experience with NLP libraries, embedding models, and vector databases
- Hands-on experience with OpenAI, Llama/Llama2 and other open-source models, and Azure OpenAI
- Experience designing and deploying scalable, distributed architectures for AI-powered applications, including microservices, RESTful APIs, and cloud-native design patterns
- Hands-on experience with at least one major cloud platform (AWS, Azure, or Google Cloud) and containerization/orchestration tools (Docker, Kubernetes)
- Experience with MLOps/LLMOps pipelines — model training, deployment, monitoring, and lifecycle management
- Demonstrated ability to design, implement, and optimize end-to-end machine learning pipelines, with strong statistical analysis and ML fundamentals
- Strong solution-design thinking — able to reason clearly through ambiguous problems and land on sound architectural decisions
- Excellent communication skills; comfortable in customer-facing conversations, translating technical concepts for non-technical stakeholders
- Strong documentation skills for architecture designs, workflows, and technical decision records
- Comfortable operating in a startup or fast-paced environment, with strong ownership and a leadership mindset
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field
- Experience with LLM fine-tuning techniques (LoRA, RLHF, PEFT) and model optimization strategies
- Familiarity with performance optimization for AI workloads — GPU/TPU acceleration, quantization, pruning, or model distillation
- Experience with AI observability and monitoring tools for tracking model performance, drift, and anomalies
- Knowledge of AI governance, security, and compliance frameworks (e.g., GDPR, SOC 2)
- Prior experience building enterprise-scale AI or LLM-based products, ideally in consulting or solution-architecture capacities
- Experience in financial services, healthcare, or insurance industries is a plus
Benefits
- Fully remote role (work from home anywhere in USA)
Company Overview