Applied AI Engineer

Key Responsibilities

• Engage directly with enterprise and strategic customers to understand their workflows, data, and technical requirements.
• Architect, build, and deploy custom solutions leveraging GenAI, LLMs, Machine Learning and Vision models, and customer data sources.
• Lead full project lifecycles: scoping, solution design, development, implementation, testing, deployment, and iteration.
• Integrate and optimize AI/ML pipelines, including data preprocessing, prompt engineering, model selection, and evaluation.
• Build reliable, scalable software integrations using APIs, cloud services, and containerized systems.
• Troubleshoot complex technical issues across the stack—applications, models, data pipelines, infrastructure, and integrations.
• Act as the customer’s trusted technical advisor, enabling adoption of new product capabilities and AI features.
• Partner closely with internal product and engineering teams to communicate customer feedback and shape roadmap direction.
• Produce high-quality documentation, architecture diagrams, runbooks, and technical assets for customer teams.
• Mentor junior engineers and contribute to internal best practices for FDE delivery.

Qualifications

• 5–10+ years in engineering roles such as Forward Deployed Engineer, ML Engineer, Software Engineer, Solutions Engineer, Technical Consultant, or similar.
• Strong proficiency in Python, JavaScript/TypeScript, Go, or similar production-oriented languages.
• Hands-on experience with Machine Learning, including training, fine-tuning, evaluating, or deploying models.
• Direct experience with Generative AI (LLMs, multimodal models) and applying them to real-world problems.
• Exposure to Computer Vision techniques (detection, segmentation, OCR, embeddings, multimodal pipelines).
• Strong knowledge of ML frameworks (PyTorch, TensorFlow, OpenCV, etc.).
• Experience with cloud infrastructure (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
• Excellent communication skills with both technical and non-technical audiences.
• Comfort leading customer-facing engagements and guiding stakeholders through ambiguity.
• Willingness and ability to travel frequently.

PREFERRED

• Experience in consulting, technical solutions, professional services, or customer-embedded technical roles.
• Experience with vector databases, embedding pipelines, or retrieval-augmented generation (RAG).
• Experience building APIs, microservices, or distributed systems.
• Familiarity with MLOps tools (Docker, Kubernetes, model registries, CI/CD for ML).
• Background in deploying or fine-tuning CV models (YOLO, SAM, CLIP, DETR, etc.).
• Experience in startup or high-growth environments.