Lead Gen AI Engineer

Job Title - Lead Generative AI Engineer

Location - Irving, TX (On-site/Hybrid)

Job Type- Full Time (Not open for C2C)

Key Responsibilities:

• Lead Generative AI Strategy: Define and implement a comprehensive AI strategy aligned with enterprise-wide goals, focusing on innovation in the Controls Technology domain.
• Build & Lead a High-Performing Team: Hire, mentor, and manage a team of AI specialists, ensuring the acquisition and retention of top talent in the AI/ML space.
• Drive AI Innovation: Collaborate with internal stakeholders to identify business challenges that can be solved through AI, creating scalable solutions using Generative AI technologies.
• AI System Development: Oversee the design, development, and deployment of AI models and algorithms, ensuring solutions are robust, efficient, and scalable.
• Cross-functional Collaboration: Work closely with the Data Mesh, Cloud Architecture, and broader tech teams to integrate AI models into existing and future architectures.
• Stay Current on AI Trends: Continuously monitor the latest AI trends and technologies to ensure the organization remains at the cutting edge of Gen AI innovations.
• Ensure Ethical AI Use: Ensure all AI initiatives comply with data privacy, ethical AI standards, and corporate governance policies.

Required Technical Skills:

• Large Language Models (LLMs) & Fine-Tuning: Deep knowledge of LLMs and advanced fine-tuning techniques and proficient in Parameter-Efficient Fine-Tuning (PEFT) methods, including LoRA, QLoRA, Adapter Tuning, and Prefix Tuning. Experience with full fine-tuning, instruction tuning strategies, and cutting-edge agentic AI techniques such as Reinforcement Learning from Human Feedback (RLHF) and multi-task learning is essential.
• Model Optimization: Expertise in model compression and quantization methods and be skilled in techniques like AWQ and GPTQ, particularly GPTQ-for-LLaMA. Proficiency in using optimized inference engines such as vLLM, DeepSpeed, and FP6-LLM is crucial for maximizing model performance and efficiency.
• Prompt Engineering: Adept at prompt engineering, demonstrating proficiency in various techniques and best practices. Familiarity with tools and frameworks that facilitate effective prompt design is necessary to guide the team in creating powerful and efficient AI interactions.
• Retrieval-Augmented Generation (RAG): Advanced knowledge of RAG techniques is required, including expertise in hybrid search methods, multi-vector retrieval, Hypothetical Document Embeddings (HyDE), self-querying, query expansion, re-ranking, and relevance filtering. This knowledge will be crucial in developing sophisticated AI systems that can leverage external knowledge effectively.
• Machine Learning Frameworks and Cloud Computing: Proficiency in TensorFlow, PyTorch, and high-level APIs like Keras is essential. Knowledge of distributed training and parallel processing frameworks is also required to handle large-scale AI projects efficiently.
• Natural Language Processing (NLP) and AI Deployment: Advanced NLP skills, including Named Entity Recognition (NER), Dependency Parsing, Text Classification, and Topic Modeling. Experience with Transfer Learning, Few-shot, and Zero-shot learning paradigms is crucial. Expertise in containerization (Docker), orchestration (Kubernetes), and experience with CI/CD pipelines for AL/ML model deployment.
• Data Science, Engineering, and API Development: Strong proficiency in data preprocessing, feature engineering, and handling large-scale datasets is required. Experience with real-time AI applications and streaming data processing is valuable. Expertise in designing and implementing RESTful APIs for seamless AI model integration.
• Generative AI Tools & Platforms: Experienced with cutting-edge generative AI tools, including LangChain for building LLM applications, LlamaIndex for context-augmented generative AI, and Hugging Face Transformers. Familiarity with Gen AI APIs like OpenAI, Gemini, Claude etc. and other relevant generative AI platforms is essential. Proficiency with version control systems like Git for collaborative AI development is also required.
• AI Compliance & Guardrails: Knowledge of AI compliance frameworks and best practices. Experience in implementing guardrails to ensure ethical AI usage and mitigate risks is crucial. Familiarity with frameworks such as Microsoft's AI Guidance Framework will be beneficial in maintaining responsible AI development and deployment practices

Required Leadership & Soft Skills:

• Team Leadership: Proven ability to build, lead, and develop high-performing AI teams, ensuring alignment with organizational goals.
• Strategic Thinking: Ability to define a long-term vision for Gen AI, articulate the value proposition, and map it to organizational objectives.
• Collaboration & Influence: Strong interpersonal skills to work effectively across technical and non-technical teams, influencing decision-making at all levels.
• Innovation Focused: Passion for staying at the forefront of AI technologies, encouraging innovation, and exploring new AI applications.
• Communication Skills: Ability to convey complex AI concepts to non-technical stakeholders, demonstrating the value and potential business impact of AI initiatives.
• Problem Solving: Proactive and analytical mindset to address challenges and identify opportunities for AI-driven optimization.

Qualifications:

• 8 years of experience in AI/ML, with at least 3 years in Generative AI.
• 5 years of leadership experience managing AI teams and delivering complex AI solutions.
• Extensive hands-on experience with AWS services and infrastructure related to AI/ML.
• Strong portfolio of AI-driven projects showcasing successful implementations of Generative AI solutions in a business context.

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