AI Strategist

Job Overview

We're seeking a visionary AI Strategist to spearhead our organization's AI transformation. As a key member of our team, you will play a pivotal role in designing and implementing cutting-edge AI solutions that drive business growth and innovation.

In this critical position, you will work closely with cross-functional teams to translate complex business challenges into actionable AI-driven strategies. Your expertise will be essential in ensuring the successful deployment of AI-powered applications in production, resulting in significant cost savings, high availability, and reliability.

Key Responsibilities

• Develop and Implement AI Roadmaps: Collaborate with stakeholders to design and implement comprehensive AI roadmaps that align with organizational goals and objectives.
• Lead AI Engineering Efforts: Spearhead the design, development, and optimization of traditional machine learning systems and LLM-based applications.
• Optimize Model Inference for Performance and Cost-Efficiency: Develop and implement strategies to optimize model inference for performance and cost-efficiency across cloud platforms.
• Fine-Tune Foundation Models: Fine-tune foundation models using LoRA, QLoRA, and adapter layers to meet client specifications.
• Develop Prompt Engineering Strategies: Design and develop prompt engineering strategies, including few-shot learning, chain-of-thought, and RAG frameworks.
• Architect Backend Infrastructure: Design and build robust backend infrastructure (APIs, microservices) to support AI-driven applications.
• Implement MLOps Pipelines: Develop and manage end-to-end MLOps pipelines, automating model training, deployment, and monitoring.
• Design Continuous Monitoring Systems: Design continuous monitoring and evaluation systems to ensure model accuracy, performance, and compliance.

Critical Success Factors

• Successful Deployment in Production: Achieve successful deployment of AI-powered applications in production with low latency and high throughput.
• Demonstrated Cost Savings: Demonstrate significant cost savings through optimized inference and resource-efficient model tuning.
• High Availability and Reliability: Ensure high availability and reliability of MLOps pipelines, reflected in reduced downtime and streamlined workflows.
• Effective Collaboration: Foster effective collaboration with cross-functional teams, resulting in on-time, on-budget project deliveries.

Education and Experience

• Bachelor's Degree in Computer Science or Related Field: Possess a bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 4+ Years of AI/ML Engineering Experience: Hold 4+ years of experience in AI/ML engineering, software development, or data-driven solution delivery.
• Proven Expertise in LLM Fine-Tuning: Demonstrate proven expertise in LLM fine-tuning (LoRA, QLoRA, adapter layers) and inference optimization.
• Strong Backend Development Experience: Possess strong backend development experience using Python with FastAPI or Flask.
• Hands-on Experience with Cloud ML Services: Have hands-on experience with cloud ML services (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes).
• Familiarity with MLOps Tools: Be familiar with MLOps frameworks, orchestration tools (Airflow), and CI/CD pipelines.

Essential Skills and CompetenciesTechnical Skills

• Proficiency in Python and ML Frameworks: Possess proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers).
• Deep Understanding of LLM Serving Tools: Have a deep understanding of LLM serving tools (vLLM, TensorRT-LLM, SGlang).
• Experience with RESTful API Development: Have experience with RESTful API development and vector/traditional databases (PostgreSQL, Redis).
• Hands-on Expertise in Container Orchestration: Possess hands-on expertise in AWS, GCP, Azure, and container orchestration (Docker, Kubernetes).
• Familiarity with MLOps Tools for Monitoring and Evaluation: Be familiar with MLOps tools for monitoring, evaluation, and CI/CD automation.

Soft Skills

• Strong Problem-Solving and Analytical Thinking: Possess strong problem-solving and analytical thinking skills.
• Excellent Communication Skills: Exhibit excellent communication skills, able to convey complex AI concepts to non-technical stakeholders.
• Adaptability and Self-Motivation: Demonstrate adaptability to shifting priorities in an agile, project-based environment.
• Collaborative Team Player: Showcase collaborative team player skills, fostering knowledge sharing and mentorship.

Behavioural Strengths

• Detail-Oriented with Quality-First Mindset: Possess a detail-oriented mindset with a quality-first approach.
• Proactive and Self-Motivated: Demonstrate proactivity and self-motivation with a passion for continuous learning.
• Leadership Skills: Exhibit leadership skills, driving end-to-end AI projects while coordinating across cross-functional teams.

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