Total 2 Others level Science / R&D / Research job vacancies in Gurgaon

Job Summary

We are seeking a highly skilled Financial Data Analyst with strong experience in Life & Annuity actuarial functions. The ideal candidate will bring deep expertise in financial data analytics, asset modeling, and programming (Python), along with a solid understanding of insurance and investment principles. This role will support actuarial modeling, financial reporting, and strategic decision-making through data-driven insights.

Key Responsibilities

• Analyze large and complex financial datasets related to life and annuity products to support actuarial valuations and reporting.

• Develop, maintain, and optimize data pipelines and analytical models using Python.

• Perform asset modeling and support Asset-Liability Management (ALM) processes.

• Collaborate with actuarial, finance, and investment teams to deliver actionable insights.

• Support financial forecasting, valuation, and risk analysis for insurance portfolios.

• Automate reporting processes and improve efficiency in data handling and analysis.

• Ensure data quality, integrity, and compliance with regulatory and internal standards.

• Assist in the development and validation of actuarial and financial models.

• Interpret and communicate analytical results to stakeholders in a clear and concise manner.

Required Qualifications & Experience

• Bachelor’s or master’s degree in actuarial science, Finance, Statistics, Mathematics, or a related field.

• 7+ years of experience in financial data analytics, preferably within life insurance or annuity domains.

• Strong hands-on expertise in Python (Pandas, NumPy, data visualization libraries).

• Proven experience in financial data analysis and reporting.

• Experience with asset modeling and/or Asset-Liability Management (ALM).

• Solid understanding of actuarial concepts in life and annuity products.

Preferred Skills

• Knowledge of insurance investment strategies and financial instruments.

• Experience working with actuarial modeling tools (e.g., Prophet, AXIS, MG-ALFA, or similar).

• Familiarity with regulatory frameworks (e.g., IFRS 17, Solvency II, US GAAP).

• Strong SQL and database management skills.

• Experience with data visualization tools (e.g., Power BI, Tableau).

• Actuarial exams progress or certification (preferred but not mandatory).

Key Competencies

• Strong analytical and problem-solving skills.

• Attention to detail and data accuracy.

• Effective communication and stakeholder management.

• Ability to work collaboratively in cross-functional teams.

• Proactive mindset with a focus on continuous improvement.

Role Overview: We are hiring an LLMOps Engineer to join our AI Research team, a highly technical group working on cutting-edge advancements in the AI industry. The team focuses on building scalable, production-grade LLM systems, fine-tuning strategies, evaluation frameworks, and next-generation deployment architectures.

This role requires hands-on experience operating LLMs beyond simple API integration. The ideal candidate understands the architectural, operational, and evaluation complexities that differentiate LLMOps from traditional MLOps.

Responsibilities:- 

1. Manage the end-to-end lifecycle of LLMs: registry, packaging, versioning, deployment, monitoring, and rollback.
2. Deploy and operate self-hosted / open-source LLMs (not limited to OpenAI API usage).
3. Design and manage scalable inference infrastructure, including GPU-aware deployments.
4. Implement CI/CD pipelines for LLM deployment and continuous evaluation.
5. Monitor system performance including latency, throughput, token usage, cost, drift (model and data), and hallucinations.
6. Ensure secure, compliant, and resilient cloud-based model deployments.
7. Collaborate with research and engineering for deployments.

Skills:-

1. Strong hands-on experience with LLM handling, hosting, and operationalization.
2. Clear understanding of how LLMOps differs from traditional MLOps (prompt management, non-deterministic outputs, semantic evaluation, token economics, guardrails etc.).
3. Experience with Kubernetes, Docker, and containerized deployments.
4. Cloud expertise (AWS / Azure / GCP) including compute, storage, IAM, networking, and monitoring.
5. Experience building scalable inference and model-serving architectures.
6. Familiarity with tools such as MLflow, Kubeflow etc. (good to have).
7. Understanding of vector databases, RAG systems, and evaluation frameworks (preferred).
8. Knowledge of GenAI security considerations (prompt injection, data leakage prevention).