About Us
We are a boutique data and AI consultancy partnering with enterprise clients to design and deliver tailored solutions that solve complex business problems. Our teams blend hands-on technical expertise with strategic advisory services, helping large organizations adopt generative AI responsibly and effectively—building prototypes, production systems, governance frameworks, and upskilling programs that align with each client’s goals and constraints. We value close client partnerships, pragmatic engineering, and measurable outcomes.
Role Overview
As a Generative AI Engineer at our consultancy, you will design, build, and deploy production-grade generative AI systems for enterprise clients. You’ll work across the model lifecycle—prototyping, fine-tuning, evaluation, scalable deployment, and monitoring—while collaborating closely with product managers, data engineers, ML researchers, and client stakeholders. The role requires strong hands-on engineering skills, practical model knowledge, and an emphasis on responsible AI, reliability, and operational excellence.
Key Responsibilities
- Design and implement generative AI solutions (LLMs, diffusion/multimodal models) tailored to client requirements and constraints.
- Prototype model architectures, perform fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and evaluation experiments to find practical solutions.
- Build scalable, secure, and maintainable model serving and inference pipelines using cloud-native architectures, containerization, and orchestration.
- Implement data pipelines for training, fine-tuning, evaluation, and continuous learning; ensure data quality, lineage, and privacy controls.
- Integrate models into client applications via robust APIs, microservices, and SDKs; collaborate with frontend and backend engineers on end-to-end product delivery.
- Implement model monitoring, drift detection, observability, logging, and automated alerts; define and track model/product-level KPIs.
- Apply responsible-AI practices: bias and fairness testing, safety filters and guardrails, differential privacy or anonymization where required, and explainability tools.
- Optimize inference cost and latency through model selection, quantization, caching, batching, and architecture/design changes.
- Produce clear technical documentation, runbooks, and client-facing knowledge transfer materials.
- Mentor junior engineers and lead technical discussions during client engagements.
Required Qualifications
- 3+ years engineering experience building production ML or AI systems; demonstrable experience with generative models or LLM-driven products.
- Strong programming skills in Python; familiarity with ML frameworks (PyTorch, TensorFlow) and libraries (Transformers, Hugging Face ecosystem).
- Practical experience with LLM workflows: fine-tuning, instruction tuning, RLHF basics, RAG, embeddings, and evaluation metrics.
- Experience deploying models to production on cloud platforms (AWS/GCP/Azure) and using containerization (Docker) and orchestration (Kubernetes).
- Knowledge of MLOps tools and practices: CI/CD for models, model versioning, feature stores, monitoring (Prometheus, Grafana, Sentry), and model registries.
- Solid software engineering practices: testing, code reviews, modular design, and API development.
- Strong problem-solving skills and the ability to translate ambiguous client needs into pragmatic technical approaches.
- Excellent communication skills for client-facing roles and cross-functional collaboration.
Preferred / Nice-to-have
- Experience with open-source and foundation models (Llama, Mistral, Falcon, Stable Diffusion, etc.) and vendor APIs (OpenAI, Anthropic, Cohere).
- Familiarity with model compression and optimization techniques (quantization, pruning, LoRA).
- Background in data engineering, retrieval systems (vector databases like Pinecone, Milvus, Weaviate), and semantic search.
- Experience with privacy-preserving ML (DP, federated learning) or enterprise security/compliance requirements.
- Advanced degree in Computer Science, Machine Learning, or related field.
- Consulting experience and proven ability to work directly with enterprise stakeholders.