Advanced AI Engineer Position Available

The ideal candidate will possess a strong understanding of large language models (LLMs), prompt engineering, and the principles behind building autonomous AI agents capable of reasoning, planning, and taking actions to achieve complex objectives.

You will be instrumental in designing, implementing, and integrating cutting-edge AI systems on leading cloud platforms (AWS, Azure, or GCP).

Responsibilities include:

• Designing, developing, and implementing advanced generative AI models (e.g., LLMs, GANs, VAEs, Diffusion Models) for diverse applications such as content creation, code generation, data synthesis, or other relevant domains.
• Architecting, developing, and deploying autonomous AI agents capable of breaking down complex goals, making decisions based on context, interacting with external tools and APIs, and taking multi-step actions to achieve objectives with minimal human intervention.
• Mastery of prompt engineering techniques to optimize the behavior and output of LLMs and design sophisticated orchestration mechanisms for coordinating multiple AI agents in complex workflows.
• Leveraging expertise in AWS, Azure, or GCP to deploy, manage, and scale Generative and Agentic AI models and pipelines. Utilizing relevant cloud services (e.g., AWS SageMaker, Azure ML, Google AI Platform, Kubernetes, serverless functions, MLOps tools).
• Training, fine-tuning, and optimizing generative and agentic AI models for performance, scalability, reliability, and efficiency, utilizing large datasets and advanced techniques.
• Integrating AI agents with various external tools, APIs, and data sources to expand their capabilities and enable them to interact with real-world systems.
• Working with large and diverse datasets, including data collection, cleaning, transformation, and feature engineering, to ensure high-quality input for both generative and agentic models.
• Staying abreast of the latest advancements in Generative AI, Agentic AI, LLMs, and multi-agent systems research. Experimenting with new frameworks (e.g., LangChain, AutoGen, CrewAI, LangGraph, Semantic Kernel) and identifying opportunities to apply novel techniques.
• Implementing robust MLOps practices for the full lifecycle of AI models, including versioning, continuous integration/continuous delivery (CI/CD), monitoring, and automated retraining.
• Developing and implementing rigorous evaluation frameworks for assessing the performance, safety, and ethical implications of generative and agentic AI systems.
• Collaborating effectively with cross-functional teams (data scientists, software engineers, product managers, business stakeholders) to translate business requirements into technical solutions and communicate complex AI concepts clearly.

Applicants should have:

• A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related quantitative field.
• 3-5 years of hands-on experience in AI/ML engineering, with a demonstrable focus on Generative AI and/or Agentic AI projects.
• Strong practical experience with Generative AI models (e.g., LLMs, Transformers, GANs, Diffusion Models) and their applications.
• Hands-on experience in designing, building, and deploying autonomous AI agents or multi-agent systems, utilizing frameworks like LangChain, AutoGen, CrewAI, or LangGraph.
• Proficiency in Python and strong experience with major ML frameworks such as TensorFlow, PyTorch, or Keras.
• Demonstrated experience with prompt engineering techniques for optimizing LLM behavior.
• Proven ability to deploy and manage AI/ML solutions on at least one major cloud platform: o AWS: Experience with services like SageMaker, EC2, Lambda, Bedrock, Open Search. o Azure: Experience with Azure Machine Learning, Azure Functions, Azure OpenAI Service. o GCP: Experience with Google AI Platform (Vertex AI), Cloud Functions.
• Solid understanding of machine learning algorithms, deep learning architectures, natural language processing (NLP), and information retrieval techniques (e.g., RAG).
• Familiarity with containerization technologies (Docker, Kubernetes).
• Experience with MLOps principles and tools for model deployment, monitoring, and lifecycle management.
• Excellent problem-solving, analytical, and critical thinking skills.
• Strong verbal and written communication skills, with the ability to articulate complex technical concepts to diverse audiences.

Candidates with experience in:

• Vector databases (e.g., Pinecone, Weaviate, FAISS, Azure AI Search) and knowledge graphs.
• Full-stack development, including building RESTful APIs using frameworks like FastAPI, Flask, or Django.
• Model evaluation tools like DeepEval, FMeval, or RAGAS.
• Prompt engineering frameworks (e.g., LangChain, LlamaIndex).
• Reinforcement learning and multi-agent reinforcement learning.
• Contributions to open-source AI projects or relevant publications.
• Data orchestration tools (e.g., Apache Airflow).