Senior Genrative Ai Engineer - Remote

Experience: 5 - 9 Years

Location: Europe - Permanent Remote

Mandatory Requirement

• . Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 5-9 years of experience in data science, ML engineering, or AI development, with 2+ years focused on Generative AI.
• Strong hands-on experience with LLMs, transformers, embeddings, and vector databases.
• Proficiency in Python and GenAI frameworks (LangChain, LlamaIndex, Haystack, Hugging Face, etc.).
• Experience with fine-tuning techniques (LoRA, PEFT, instruction tuning).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with MLOps tools, CI/CD pipelines, and model monitoring.
  
  

Candidate MUST be from Life Science background - Pharmaceutical, Consumer Care, MedTech, Hospitals, Clinics and Health insurance - any

Role Overview

The GenAI Engineer will be responsible for building and operationalizing Generative AI capabilities across platforms and client engagements. This role blends hands- on model development, prompt and pipeline engineering, MLOps, and business-facing solution design.

The ideal candidate combines deep technical expertise in LLMs and GenAI frameworks with strong business understanding, enabling them to translate real-world problems into scalable GenAI solutions with measurable impact.

Key Responsibilities

GenAI Model Development G Engineering

Design, train, fine-tune, and evaluate Generative AI models (LLMs, multimodal models) for enterprise use cases.

Develop and optimize prompt engineering, RAG pipelines, agents, and fine-tuning workflows.

Work with open-source and commercial LLMs (OpenAI, Anthropic, LLaMA, Mistral, etc.).

Implement guardrails, safety mechanisms, and hallucination mitigation techniques.

Use Case Definition G Business Enablement

Partner with business, consulting, and product teams to identify, evaluate, and prioritize GenAI use cases.

Translate business problems into clear GenAI solution architectures and success metrics.

Define ROI, feasibility, and scalability of GenAI initiatives.

Create solution blueprints, prototypes, and POCs to demonstrate business value.

Data, MLOps G Deployment

Design and manage data pipelines for GenAI training, fine-tuning, and inference.

Build scalable, secure, and cost-efficient GenAI systems for production environments.

Collaborate with engineering teams on deployment, monitoring, and retraining strategies.

Monitor model performance, latency, cost, and drift in production.

Governance, Risk G Responsible AI

Ensure responsible, ethical, and compliant use of Generative AI.

Implement explainability, auditability, and traceability mechanisms where required.

Address data privacy, IP protection, and regulatory constraints (e.g., GDPR).

Define and enforce GenAI best practices, standards, and usage guidelines.

Ǫualifications Experience

5-9 years of experience in data science, ML engineering, or AI development, with 2+ years focused on Generative AI.

Strong hands-on experience with LLMs, transformers, embeddings, and vector databases.

Proficiency in Python and GenAI frameworks (LangChain, LlamaIndex, Haystack, Hugging Face, etc.).

Experience with fine-tuning techniques (LoRA, PEFT, instruction tuning).

Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).

Familiarity with MLOps tools, CI/CD pipelines, and model monitoring.

Experience Working In Consulting Or Enterprise Product Environments (preferred).

Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.

Key Skills Attributes

GenAI Expertise: Deep understanding of LLMs, RAG, agents, and multimodal AI.

Business Orientation: Ability to define GenAI use cases tied to measurable business outcomes.

Problem Solving: Translates ambiguity into structured AI solutions.

Engineering Mindset: Builds scalable, secure, and production-ready systems.

Communication: Effectively explains GenAI concepts to non-technical stakeholders.

Ownership: Takes end-to-end responsibility from idea to production.

Adaptability: Thrives in a fast-paced, rapidly evolving AI landscape.

Ethical Awareness: Strong focus on responsible AI and governance.