Senior Data Scientist Engineer

What Will Your Role Look Like

Develop end-to-end data science and GenAI solutions for enterprise data products.

Design, build, and deploy predictive models, advanced ML algorithms, and GenAI systems that drive actionable insights.

Collaborate with business teams to understand analytics needs and translate them into technical solutions.

Work on RAG pipelines, knowledge graphs, and hybrid AI search systems.

Conduct research, rapid prototyping, and experimentation on state-of-the-art AI/ML/LLM technologies.

Monitor and optimize model performance in production using modern MLOps practices.

Mentor junior team members and contribute to best practices in AI/ML development.

Why You Will Love This Role

Opportunity to work on cutting-edge AI/ML and GenAI technologies like LLMs, LangChain, vector DBs, and advanced deep learning frameworks.

Freedom to innovate, experiment, and build industry-leading AI solutions.

Work in a collaborative environment with product managers, architects, and cross-functional teams.

Access to enterprise cloud ecosystems such as AWS, Azure, or GCP for full-scale model development and deployment.

Be part of impactful AI initiatives that directly contribute to business growth and digital transformation.

Culture that values entrepreneurial mindset, proactiveness, and innovation.

We Would Like You to Bring Along

5+ years of experience in data analytics, data science, AI/ML, or NLP roles.

Strong proficiency in Python (mandatory) or R.

Hands-on experience with traditional and modern ML techniques Regression, Classification, Ensemble models, Deep Learning, NLP/NLU, Forecasting, Anomaly Detection.

Expertise with deep learning frameworks: TensorFlow or PyTorch.

Experience building GenAI solutions using LangChain/LlamaIndex and modern LLM models (OpenAI, Mistral, Llama, Anthropic, etc.).

Strong understanding of model evaluation, validation, and alignment with business objectives.

Experience with SQL, Pandas, Spark, and feature store technologies (Feast/Tecton).

Cloud expertise with AWS/Azure/GCP, ideally including AI services, SageMaker, Vertex AI, or Bedrock.

Familiarity with MLOps tools like MLflow, Kubeflow, SageMaker pipelines.

Ability to translate business problems into analytical solutions with a strong ownership mindset.

Fluent in English and comfortable working in Agile environments.

Good-to-Have Skills

Knowledge of multi-modal AI (vision-language, diffusion models, speech AI).

Experience with knowledge graph construction and semantic search.

Prior experience with domain-specific model fine-tuning or custom pipeline orchestration.

Contributions to open-source AI/ML projects, publications, or participation in AI communities.

Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus.