Data scientist who can think out of the box and expects more from their career.

• Research business domain and develop use-cases to support enterprise wide AI/ML solutions.
• Model design, feature planning, system infrastructure, production setup and monitoring, and release management.
• Excellent understanding of machine learning techniques and algorithms, such as SVM, Decision Forests, k-NN, Naive Bayes etc.
• Experience in selecting features, building and optimizing classifiers using machine learning techniques.
• Prior experience with data visualization tools, such as D3.js, GGplot, etc..
• Good knowledge on statistics skills, such as distributions, statistical testing, regression, etc..
• Adequate presentation and communication skills to explain results and methodologies to non-technical stakeholders.
• Test, troubleshoot and enhance the developed models in a distributed environments to improve it's accuracy.
• Work closely with product teams to implement algorithms with Python and/or R.
• Design and implement scalable predictive models, classifiers leveraging machine learning, data regression.
• Facilitate integration with enterprise applications using APIs to enrich implementations.

4-7 years of experience in Data Science / Applied ML / NLP with hands-on GenAI delivery.
Strong Python skills; experience with FastAPI/Flask (or similar) for serving.
Practical experience with LLMs, including:
RAG pipelines (vector DB + embeddings + retrieval + grounding)
Prompt engineering and structured outputs (JSON schema/function calling patterns)
Experience building or integrating agents/tool-use systems (planning + tool execution + retries + state management).
Knowledge of NLP fundamentals: tokenization, embeddings, similarity search, ranking.
Proficiency with SQL and data processing (pandas / Spark basics).
Experience with LangChain / LlamaIndex / Semantic Kernel (or similar orchestration frameworks).
Familiarity with vector databases: Weaviate/PGVector
Knowledge of LLM safety: prompt injection defense, data exfiltration prevention, moderation filters, sandboxing tools.
Experience with cloud platforms: Azure especially managed AI services.
Familiarity with MLflow, Weights & Biases, or similar tracking tools.

Tech Stack

Languages: Python, SQL
LLM/GenAI: OpenAI/Azure OpenAI or open-source (Llama/Mistral), Hugging Face
Orchestration: LangChain / LlamaIndex / Semantic Kernel
Vector Search: Weaviate/PGVector
Backend: FastAPI, Docker, Kubernetes (optional)
MLOps/Obs: MLflow/W&B, Prometheus/Grafana, Open Telemetry (optional)
Data: Postgres, S3/ADLS

“We are committed to creating a diverse environment and are proud to be an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status”.

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