Quantitative R&D Engineer

We are hiring for a Python Developer at Wissen Technology!

📍 Location: Pune (Hybrid)

💼 Experience: 3–6 Years

⏱️ Notice Period: Immediate / 15 days preferred

🔧 Key Skills:

• Strong experience in Python

• Hands-on with Pandas & NumPy

• Experience with AWS (S3, Lambda preferred)

• Good understanding of data processing & APIs

• SQL knowledge

🏢 About Wissen Technology:

Wissen Technology, part of the Wissen Group (est. 2000), is a fast-growing technology company specializing in high-end consulting across Banking, Finance, Telecom, and Healthcare domains.

✔️ Global presence – US, India, UK, Australia, Mexico & Canada

✔️ Certified Great Place to Work®

✔️ Trusted by Fortune 500 clients like Morgan Stanley, Goldman Sachs, and more

✔️ Strong growth with 400% revenue increase in recent years

🌐 Website: www.wissen.com

🔗 LinkedIn: https://www.linkedin.com/company/wissen-technology/

If you’re interested or have relevant candidates, please share your resume at [your email].

#Hiring #PythonDeveloper #PuneJobs #AWS #ImmediateJoiner

While you may already know about Wissen and the company history, here is a quick rundown for you.

About Wissen Technology:

·        The Wissen Group was founded in the year 2000. Wissen Technology, a part of Wissen Group, was established in the year 2015.

·        Wissen Technology is a specialized technology company that delivers high-end consulting for organizations in the Banking & Finance, Telecom, and Healthcare domains. We help clients build world class products.

·        Our workforce has highly skilled professionals, with leadership and senior management executives who have graduated from Ivy League Universities like Wharton, MIT, IITs, IIMs, and NITs and with rich work experience in some of the biggest companies in the world.

·        Wissen Technology has grown its revenues by 400% in these five years without any external funding or investments.

·        Globally present with offices US, India, UK, Australia, Mexico, and Canada.

·        We offer an array of services including Application Development, Artificial Intelligence & Machine Learning, Big Data & Analytics, Visualization & Business Intelligence, Robotic Process Automation, Cloud, Mobility, Agile & DevOps, Quality Assurance & Test Automation.

·        Wissen Technology has been certified as a Great Place to Work®.

·        Wissen Technology has been voted as the Top 20 AI/ML vendor by CIO Insider in 2020.

·        Over the years, Wissen Group has successfully delivered $650 million worth of projects for more than 20 of the Fortune 500 companies.

·        We have served client across sectors like Banking, Telecom, Healthcare, Manufacturing, and Energy. They include likes of Morgan Stanley, Goldman Sachs, MSCI, StateStreet, Flipkart, Swiggy, Trafigura, GE to name a few.

De

Job Title: Application Development Engineer (Python – Backtesting & Index Platforms)

Role Overview

Key Responsibilities

Engine Development: Design and implement modular, reusable Python components for index construction, rebalancing, and backtesting.

Large-Scale Simulation: Use Pandas, NumPy, and PySpark to run historical calculations across long time horizons and multiple index variants.

Workflow Integration: Integrate engines with orchestrators such as Airflow or Temporal using parameterized, config-driven execution.

Reference Data Consumption: Query and utilize pricing, security master, and corporate action data from Snowflake.

Quality & Reconciliation: Build automated test harnesses to validate outputs, compare against benchmarks, and guarantee reproducibility.

Performance Optimization: Improve runtime efficiency through vectorization, caching, and distributed computing patterns.

Cross-Team Collaboration: Partner with Business, Index Ops, and Platform teams to accelerate research-to-production onboarding.

Required Technical Capabilities

Python Expertise: Strong proficiency in Python application development with emphasis on clean architecture and maintainable design.

Data & Numerical Libraries: Deep experience with Pandas and NumPy; working knowledge of PySpark for distributed workloads.

Financial Computation: Ability to implement portfolio mathematics, weighting algorithms, and time-series transformations.

Config-Driven Systems: Experience building rule-based or metadata-driven processing frameworks.

Database Skills: Strong SQL and experience consuming structured data from Snowflake.

Testing Discipline: Expertise in unit testing, regression testing, and deterministic replay of calculations.

Orchestration Integration: Familiarity with Airflow, Temporal, or similar workflow engines.

Cloud Infrastructure: Solid understanding of AWS ecosystem services (S3, Lambda, IAM) and how they integrate with the Snowflake Data Cloud.

We are a San Francisco-based AI infrastructure company working with leading frontier AI labs to build post-training data and evaluation infrastructure for foundation models. We are hiring a Python Developer to create high-quality datasets, reinforcement learning environments, and benchmarking pipelines used to improve and evaluate state-of-the-art LLMs. This is a remote role with flexible working hours.

Responsibilities

* Create and curate datasets for LLM post-training (SFT, RLHF, RL, preference optimization).

* Build and maintain RL environments for agent evaluation.

* Develop Python tooling for dataset generation, validation, and transformation.

* Evaluate models on custom benchmarks and testing pipelines.

* Collaborate with research and engineering teams to deliver client-specific post-training datasets.

* Work with terminal-first development workflows and cloud infrastructure.

Required Skills

* Strong Python programming skills.

* Understanding of LLM fundamentals and post-training concepts (SFT, RLHF, RL).

* Experience working with structured data (JSON, CSV, YAML).

* Git, Linux/Unix command line, and solid software engineering fundamentals.

Good to Have

Experience with RAG, agentic AI systems, Hugging Face Transformers, LoRA/PEFT, LangChain or LlamaIndex, vector databases (FAISS, Qdrant, Milvus, Pinecone, Weaviate, ChromaDB), Docker, AWS/GCP, FastAPI/Flask, Bash, CLI tooling, model evaluation frameworks, benchmarking, and AI infrastructure.

Compensation

Base Salary: USD $1,250/month

Equity: ESOP/Equity package included.

Performance Bonuses: Up to USD $4,000/month (in addition to base salary).

Location

Remote (Worldwide)

Work Hours

Flexible, remote-first, asynchronous work environment.

How to Apply

Apply here: https://tally.so/r/wLReJG

Please complete the application form and submit the required details. Only shortlisted candidates will be contacted.

We are looking for a hands-on Lead Data Scientist with strong analytical, machine learning, and problem-solving skills to work in a client-facing environment.

The role requires someone who can independently identify opportunities, formulate hypotheses, design solutions, and drive initiatives from analysis through experimentation and production implementation. The candidate should also be comfortable guiding team members and working closely with engineering and client stakeholders.

Responsibilities: 

• Analyse complex datasets to identify patterns, issues, and opportunities.
• Formulate and validate hypotheses through structured analysis and experimentation.
• Build and improve machine learning and anomaly detection solutions.
• Design end-to-end solutions considering data, modelling, engineering, and production constraints.
• Perform root cause analysis across models, data, and systems.
• Work with engineering teams on feature pipelines, model inference, and production deployment.
• Lead technical discussions with clients and communicate recommendations, trade-offs, and expected impact.
• Guide team members on analysis, modelling, and solution design.
• Proactively identify initiatives and roadmap items that can add value to the project.

Requirements: 

• Strong foundation in statistics and machine learning.
• Strong hands-on experience with Python and SQL.
• Hands-on experience with Python ML and data frameworks such as Pandas, NumPy, scikit-learn, TensorFlow/Keras, Dask, Matplotlib, Boto3 SageMaker Python SDK, and Horovod.
• Strong data analysis, hypothesis-generation, and problem-solving skills.
• Experience with standard supervised and unsupervised ML techniques.
• Experience with anomaly detection techniques such as Isolation Forest and Autoencoders.
• Experience building and deploying production ML solutions.
• Working knowledge of data engineering and real-time / batch inference environments.
• Strong communication and stakeholder-management skills.
• Ability to lead technical work and guide cross-functional teams.

Good to Have: 

• Experience in fraud detection or risk modelling.
• Experience with Graph Neural Networks.
• Exposure to real-time systems, streaming features, and low-latency data stores.
• Experience in AdTech, e-commerce, gaming, mobile applications, or similar high-volume consumer platforms.

Role Overview

We are looking for a Python Developer with strong experience in Generative AI and LLM-based applications. The candidate should have hands-on experience building AI solutions using Python, RAG, LangChain/LangGraph, and related GenAI technologies.

Mandatory Skills

Python, GenAI/LLM, RAG, LangChain/LangGraph, Agentic AI, FastAPI, REST API, Vector Database, Prompt Engineering, Microservices

Key Responsibilities

• Develop and maintain applications using Python and modern frameworks.
• Build GenAI/LLM-based applications and solutions.
• Develop RAG pipelines using vector databases.
• Work with LangChain/LangGraph for LLM and agent-based applications.
• Develop and integrate REST APIs using FastAPI.
• Implement Agentic AI workflows and AI-powered features.
• Integrate LLMs with existing applications and microservices.
• Apply prompt engineering techniques to improve AI application performance.

Primary Roles & Responsibilities

▸ At SDE-2 you will build, and maintain end-to-end RAG pipelines — from ingestion and chunking to retrieval and LLM response synthesis 

▸ Design and develop MCP (Model Context Protocol) servers that power AI agents and multi-step workflows 

▸ Development on Next.js / React.js frontend modules for our AI product suite 

▸ Own PostgreSQL database design — schema modelling, indexing strategy, query optimisation, and migration management 

▸ Integrate third-party AI APIs (Gemini, OpenAI, Anthropic, etc.) and open-source models into production-grade applications  

▸ Work directly with Founders for product and leadership to scope features, estimate effort, and make architectural recommendations 

▸ Stay ahead of the AI engineering curve — evaluate new tools, frameworks, and models and bring recommendations to the team 

▸ Deep hands-on experience with RAG: multi-stage retrieval, re-ranking, hybrid search, prompt engineering 

▸ Experience building or extending MCP servers — understanding of tool calling, context passing, and agent orchestration 

▸ Strong proficiency in Next.js and React.js — you've built production UIs with proper state management, routing, and performance considerations 

▸ PostgreSQL expertise: advanced queries, indexing, schema design for complex domains, migrations in production 

▸ TypeScript proficiency — you write it by default, not as an afterthought 

▸ Experience with at least one vector database (pgvector, Pinecone, Weaviate, Qdrant) 

Good to Have: 

▸ Experience with streaming LLM responses, tool/function calling, and multi-agent frameworks 

▸ Familiarity with LangChain, LlamaIndex, or custom agentic implementations 

▸ Exposure to cloud infrastructure (AWS / GCP / Azure) and containerisation (Docker) 

▸ Knowledge of frontend design systems, Tailwind CSS.

▸ Prior experience working directly with product or business stakeholders 

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will drive the research behind our imaging products, finding approaches to hard, unsolved problems in product and apparel imagery that work at production scale. You will run the experiments, prove what is viable, and hand a working approach to the engineering team.

Who We Are Looking For

•     Total experience: 3 years or more, with a strong research orientation

•     Deep learning frameworks in Python: PyTorch or TensorFlow

•     Image processing in Python: OpenCV, Pillow, scikit-image

•     Working knowledge of diffusion and other image generation models

We are looking for a strong research or research-student profile: someone who investigates, experiments and proves an approach, working closely with the AI Architect. Someone who is driven to build solutions, not just desk research.

AI Skills and Experience

•     Computer vision: classical CV alongside deep learning.

•     Segmentation, image-to-image translation, geometry and lighting; 

•     Generative imaging: diffusion models, conditioning and control, fine-tuning and LoRA,

•     Reads academic papers, judges what is reproducible, and turns one into a working prototype in days

Good to have

•     3D and rendering; published research or open-source contributions; model optimisation for inference cost

Research and innovative problem solving

•     Comfortable where there is no known answer, and defines the approach yourself

•     Solves problems inventively rather than reaching for the biggest model; many results come from classical image processing, fitment and geometric transformation

Other Relevant Skills and Experience

•     Designs experiments: baselines, measurable success criteria, honest reporting of negative results

•     Explains findings to a non-research audience and guides engineers to production

•     Git and reproducible experiment tracking (Weights & Biases, MLflow or similar)

Educational Qualification

•     BE / B.Tech / ME / M.Tech in Computer Science

•     BE / B.Tech / ME / M.Tech in any discipline with proven Computer Vision coursework or work

•     MSc / MS in Computer Science, Maths, Statistics or Computer Vision

•     PhD in Computer Vision or Machine Learning: an advantage, not a requirement

•     Reputed Tier 1 university preferred

Are you looking to work in the cutting edge area of applying data-science to help global customers get a better insight into their health? If so, read on and apply.

Role Name: Senior Data Scientist

Science Team | Full-Time | In-Office | Bangalore

The Role

The Ultrahuman Science Team builds the algorithms behind the Ring, M1 CGM, blood and urine biomarkers, and Performance Lab assessments. We are hiring a Senior Data Scientist to own those algorithms end to end: from the raw sensor signal to a model that is shipped, monitored, and trusted in users' hands.

This is a build role with real scope. In a typical month you will improve a production algorithm, root-cause a metric users are complaining about, and stand up the data pipeline the next model needs. The common thread is ownership: you take a vague question and return a working answer, without waiting to be handed scope.

What You'll Do

·      Own algorithms end to end: sleep staging, activity detection, sensor-derived metrics, and health scores. You frame the problem, build the features, train and evaluate the model, and see it live

·      Ship models, not notebooks: you prove a change on our own cohort before it reaches users, and a model is done only when it runs in production and you can tell how it is behaving

·      Validate against reference standards: design evaluations against gold standards, reference devices, and study ground truth, and know when a result is real and when it is an artifact

·      Own the data layer: cohort extraction, feature pipelines, study data, and raw sensor data, so the next model starts from clean inputs

What This Looks Like in Practice

1. Improving production algorithms - Take an existing production model like sleep staging, root-cause the failure modes against reference data, and ship a fix you can defend with numbers.

2. Building new models - Train an activity classifier on raw sensor data, design the labeled data collection that expands it, and pick the operating point so false positives never erode trust.

3. Proving it before it ships - Run a new steps algorithm against reference-device cohorts, decide with data when it is ready, and monitor how it behaves after rollout.

Who You Are

The two things we can't coach

·      High ownership, end to end: you take a problem from a vague question to a shipped model without waiting to be handed scope, and you can point to something you owned from raw data all the way to production

·      Hungry for more scope: you have outgrown your current role and want problems biggerthan your title, with the technical depth to be trusted with them

Also important

·      You've worked with human health data: wearables, physiological signals, or clinical data.

If your experience is close but not exact, show us why you will ramp fast

·      You've built at a startup: or somewhere small enough that nobody handed you clean data, clear specs, or a mature ML platform

·      You work like it's 2026: coding agents and AI tooling are part of how you build every day, and you can tell which new capabilities are worth adopting

·      You communicate: you can explain a model and its limits to a product manager, an engineer, or a founder, and hold your own with our scientists Core Technical Skills

·      Languages and data: Python and SQL daily, comfortable working in a real codebase

·      Machine learning: PyTorch or TensorFlow, scikit-learn, and gradient boosting, with the judgment to know which the problem needs

·      Advanced machine learning: time series and sequence models, deep learning on continuous physiological signals, and ensembles

·      Statistics and evaluation: hypothesis testing, experiment and A/B design, model evaluation, and error analysis against a reference standard

·      Scale and cloud: Spark or equivalent on large datasets, and AWS, GCP, or Azure

·      Production ML and MLOps: training pipelines, model versioning, deployment, monitoring, and drift detection

·      LLMs and agentic systems: fine-tuning and serving models, building agentic pipelines, and using coding agents to move faster

Experience:

- 4 to 5 years building and shipping machine learning systems. We index on what you have shipped and on trajectory, not the exact number of years; if you are a little earlier but have clearly outgrown your current scope, we want to hear from you.

- Bachelor's or higher in engineering, computer science, statistics, or a related field.

How We Work and Who Thrives Here

- The Science team is small and moves fast, and much of the work has no precedent to copy.

- People do their best work here when they are energized by ambiguity, low on ego, quick to adopt a better idea no matter where it comes from, and comfortable owning something before anyone has told them how. If you need a mature data org, clean labelled datasets, and clear guardrails to thrive, this particular role will not be the right fit, and that is worth knowing up front.

What You'll Gain

·      Ownership of algorithms that hundreds of thousands of people see every morning

·      A dataset most scientists never get to touch: 100M+ nights of sleep and continuous physiological signals at scale

·      Direct collaboration with the engineering, product, and design teams building Ultrahuman