Senior AIML Engineer

Senior AIML Engineer (LLM & Client-Facing)

Work Mode: Hybrid

Location: Kolkata

What Youll Do

Design & Architect AI Solutions

Architect and implement advanced LLM systems (RAG, fine-tuning, agents, copilots) from concept to production.

Design end-to-end AI pipelines encompassing data processing, model evaluation, deployment, and monitoring.

Select and optimize the use of commercial and open-source LLMs (e.g., GPT, Claude, LLaMA) for performance, cost, and security.

Build scalable, secure, and cost-efficient cloud-based AI architectures.

Lead Client Delivery & Advisory

Act as the primary technical point of contact for enterprise clients, translating business needs into technical requirements and solutions.

Lead workshops, solution walkthroughs, and presentations for both technical and executive audiences.

Own project delivery timelines, manage risks, and ensure projects are delivered on time and within scope.

Drive Team Leadership & Growth

Mentor and coach junior and mid-level data scientists and ML engineers.

Contribute to hiring, onboarding, and the establishment of technical standards and best practices.

Lead and scale a high-performing AI delivery team, fostering a culture of excellence.

Establish robust practices for code quality, documentation, model governance, and MLOps.

What Were Looking For

Must-Have Qualifications

4+ years of professional experience in data science, machine learning, or AI engineering.

Proven, hands-on experience building and deploying LLM applications (RAG, agents, fine-tuning) in production environments.

Expertise in Python and deep learning frameworks (PyTorch or TensorFlow).

Deep technical knowledge of prompt engineering, retrieval-augmented generation (RAG) architectures, fine-tuning, and inference optimization.

Solid experience deploying and monitoring models in production on a major cloud platform (AWS, Azure, or GCP).

Proven client-facing, consulting, or stakeholder management experience with excellent communication and presentation skills.

Nice-to-Have Qualifications

Experience directly mentoring or leading technical teams.

Strong MLOps skills (CI/CD, containerization, model monitoring) using tools like MLflow, Weights & Biases, etc.

Experience working in regulated industries (finance, healthcare, enterprise SaaS).

Knowledge of AI governance, responsible AI, data privacy, and security standards.

Experience scoping projects, estimating timelines, and writing technical proposals. .