Note: The job is a remote job and is open to candidates in USA. EazyML is a company recognized by Gartner for its expertise in Responsible AI, focusing on enabling proactive compliance and sustainable automation for enterprises adopting AI. They are seeking a Senior GenAI/Agentic AI Engineer to design, build, and deploy scalable Generative AI systems, working closely with customers and internal teams to translate business requirements into effective AI solutions.
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
Company Overview