Senior Databricks Engineer building production data pipelines for V4C’s enterprise consultancy clients. Owning data quality, PySpark transformations, monitoring, debugging, and Lakehouse performance.
About the role
- Senior Forward Deployed Engineer at Nexxa.ai translating technical challenges into AI-powered solutions. Fostering relationships with customers while leading end-to-end project lifecycles with deep technical integration.
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.
Requirements
- 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.
- Prior experience in consulting, technical solutions, professional services, or customer-embedded technical roles (preferred).
- Experience with vector databases, embedding pipelines, or retrieval-augmented generation (RAG) (preferred).
- Experience building APIs, microservices, or distributed systems (preferred).
- Familiarity with MLOps tools (Docker, Kubernetes, model registries, CI/CD for ML) (preferred).
- Background in deploying or fine-tuning CV models (YOLO, SAM, CLIP, DETR, etc.) (preferred).
Benefits
- Innovative Environment: Play a critical role in transforming heavy industries through groundbreaking AI and automation technologies.
- Collaborative Culture: Be part of a team that values innovation, discipline, and continuous improvement.
- Professional Growth: Benefit from significant opportunities for career development and advancement.
- Competitive Compensation: Enjoy a comprehensive salary and equity package reflective of your expertise and contributions.
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