Gramian Consultancy is looking for a GenAI Solution Architect / Presales Engineer to partner directly with customers and lead the end-to-end delivery of high-impact technical solutions. In this role, you will translate real-world challenges into production-ready systems leveraging Generative AI, Computer Vision, and Machine Learning. This position blends software engineering, ML engineering, architecture, and consulting.

What You'll Do

Lead the backend and AI components of a new, HIPAA-compliant clinical intelligence platform in the urology domain.
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.

What We're Looking For

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, vector databases, or RAG) 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.
Prior experience in consulting, technical solutions, professional services, or customer-embedded technical roles.

Nice to Have

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.