Quantitative Research & Development Engineer

About the company

The client is a quantitative investment firm focused on Indian financial markets. They operate a multi-strategy, multi-manager platform designed to generate consistent, risk- adjusted returns.

Their approach combines systematic investment methods, rigorous quantitative research and institutional-grade manager evaluation. We bring together research, technology and data to build scalable investment solutions.

Role Overview

We are seeking a Quantitative Developer with strong C++ and Python expertise to convert mathematical models and research prototypes into reliable, high-performance analytical engines.

You will work closely with Quantitative Research, Data Engineering, AI and Product Engineering teams throughout the full model lifecycle—from research handover and production implementation to validation, deployment and ongoing support.

This role is ideal for someone who enjoys working at the intersection of quantitative finance, numerical computing and production software engineering.

Key Responsibilities

Research Production

• Translate mathematical models and Python research prototypes into robust, production-quality C++.
• Develop reusable components for risk analytics, forecasting, portfolio analysis and simulation.
• Build efficient Python interfaces for C++ components using pybind11 or similar technologies.
• Ensure production implementations remain mathematically and numerically consistent with the underlying research.
• Establish clear and reproducible processes for transitioning models from research to production.

Engine Development and Validation

• Design and develop analytical engines capable of processing historical, batch and streaming data.
• Integrate calculation components with data pipelines, APIs, databases and downstream applications.
• Validate production implementations against research prototypes, benchmark datasets and expected results.
• Develop automated numerical, unit, integration, regression and performance tests.
• Identify and resolve numerical stability, precision and edge-case issues.
• Optimize calculation speed, memory usage, concurrency and scalability.
• Profile and benchmark critical components to meet defined performance requirements.

Deployment and Delivery

• Package analytical engines as libraries, services, APIs or containers.
• Support deployment across internal infrastructure and client-controlled environments.
• Configure engines for different datasets, workflows and institutional requirements.
• Assist with integration testing, production upgrades, issue diagnosis and technical troubleshooting.
• Implement appropriate logging, monitoring and error-handling capabilities.
• Document interfaces, assumptions, configurations, dependencies and deployment requirements.

Collaboration and Ownership

• Work closely with Quantitative Research, Data Engineering, AI and Product Engineering teams.
• Participate in technical design discussions, code reviews and quantitative model reviews.
• Communicate implementation trade-offs, constraints and risks clearly to technical and quantitative stakeholders.
• Take end-to-end ownership of assigned components, from research handover through production deployment and support.
• Contribute to engineering standards, reusable libraries and development best practices.

Required Qualifications

• Bachelor’s or master’s degree in Computer Science, Engineering, Mathematics, Statistics, Physics, Quantitative Finance or a related discipline.
• Strong professional programming experience in modern C++, including object- oriented and generic programming.
• Proficiency in Python and scientific-computing libraries such as NumPy, pandas or SciPy.
• Experience translating mathematical or analytical prototypes into production software.
• Strong understanding of algorithms, data structures, software architecture and design principles.
• Experience building automated unit, integration and performance tests.
• Familiarity with numerical methods, floating-point behaviour and numerical validation.
• Experience profiling and optimizing compute-intensive or data-intensive applications.
• Proficiency with Git and modern software-development practices.
• Strong analytical, debugging and problem-solving skills.
• •Ability to work effectively with both researchers and software engineers.

Preferred Qualifications

• Experience with pybind11, Boost.Python, Cython or similar interoperability technologies.
• Knowledge of quantitative finance, portfolio analytics, risk modelling, forecasting or simulation.
• Familiarity with time-series data and financial-market datasets.
• Experience developing applications that process batch or real-time streaming data.
• Exposure to concurrent, parallel or distributed computing.
• Experience with containerization and deployment technologies such as Docker.
• Familiarity with Linux environments, CI/CD pipelines and cloud or on-premises infrastructure.
• Experience building analytical libraries, calculation services or APIs for institutional users.
• Knowledge of Indian financial markets is advantageous

We are hiring high-caliber engineers with 3+ years of experience to build low-latency, high-throughput trading infrastructure in Rust, working directly under senior systems architects. This is a hands-on coding role on performance-critical systems — you will write, test, and ship the core code.

Responsibilities

1. Develop and maintain high-performance backend services in Rust under the guidance of senior engineers
2. Implement components of distributed, fault-tolerant systems handling high transaction volumes
3. Write extensive unit, property-based, and simulation tests for correctness-critical code - Profile and optimize code for latency, throughput, and memory efficiency - Participate in design discussions and rigorous code reviews
4. Debug and resolve issues in Linux-based production environments

Required Qualifications

1. B.Tech/M.Tech in Computer Science from an IIT or NIT (mandatory) - 3+ years of professional software development experience in Rust, C++, or Java/Go with systems-level work; strong Rust proficiency or demonstrated ability to attain it fast - Understanding of financial markets and trading systems — order books, order types, matching concepts, spot and derivatives (futures/options) basics; hands-on experience with trading, broking, or market data systems is a strong plus - Excellent computer science fundamentals — data structures, algorithms, operating systems, networking, concurrency
2. Solid grasp of multithreading and memory management; exposure to performance profiling and benchmarking
3. Understanding of distributed systems concepts — replication, consistency, message queues (Kafka/NATS or similar)
4. Experience with Linux, Git, and modern development workflows
5. Strong problem-solving ability and hunger to work on hard systems problems

Preferred

1. Competitive programming background (Codeforces/ICPC/CodeChef ratings welcome on the CV)
2. Personal or open-source Rust projects
3. Exposure to low-latency techniques — lock-free structures, zero-allocation design, binary protocols (SBE/Protobuf/FlatBuffers)
4. NISM certifications, trading experience (personal F&O/options trading counts), or coursework in financial engineering
5. Exposure to consensus algorithms (Raft), event sourcing, or deterministic testing

Growth Path

You will be mentored directly by senior systems engineers with deep exchange-infrastructure experience, with a defined progression to senior engineer ownership of subsystems within 18–24 months. This team is being built as a center of excellence in low-latency systems engineering in India.

Selection Process

1. Resume shortlisting (college + evidence of strong engineering: projects, CP ratings, internships)
2. Online DSA + systems assessment
3. Live coding round (Rust/C++, performance-oriented problem)
4. Systems and trading-concepts discussion round
5. Culture and ownership round with the senior engineer you will report to

   Compensation: top of market for the experience band, benchmarked against product companies, not services.

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.

About PortOne

PortOne is building the reconciliation and data intelligence layer for payments across Korea and international markets. We are a Series B startup backed by Softbank and Hanwa Capital, powering multi-billion dollars in annualised settlement volume for 2,000+ merchants across Korea, Thailand, Singapore, Indonesia, and beyond.

We are building AI-native products for leading brands — intelligent automation layers on top of complex financial data pipelines. If you want to work at the intersection of fintech, data engineering, and applied AI, this is your role.

Culture and Values

* You will be joining a team that stands for making a difference.

* You will be joining a culture that identifies more with Sports Teams rather than a 9 to 5 workplace.

* Your will have peers who are/have

** Highly Self Driven with A sense of purpose

** High Energy Levels - Building stuff is your sport

** Ownership - Solve customer problems end to end - Customer is your Boss

** Hunger to learn - Highly motivated to keep developing new tech skill sets

Your Work Ethic

* You are an athlete and building apps is your sport.

* Your passion drives you to learn and build stuff and not because your manager tells you to.

* You obsess over correctness — a bug in a settlement figure or a silent data drop is not acceptable to you.

* You have an eye for detail that most engineers skip past, and you take pride in getting it exactly right.

* Your work ethic is that of an athlete preparing for your next marathon. Your sport drives you and you like being in the zone.

* You are NOT a clockwatcher renting out your time, and NOT have an attitude of "I will do only what is asked for"

What will you do?

• Build and maintain financial data ingestion pipelines that pull settlement and transaction data from marketplace platforms (Amazon, Shopee, TikTok, Qoo10, Rakuten) on behalf of large brands operating across multiple Asian markets.
• Own reconciliation workflows end-to-end — from raw marketplace data to verified, merchant-ready settlement reports — ensuring every figure is correct and every discrepancy is surfaced, not swallowed.
• Design and implement AI-native features that automate financial analysis: agentic triage of settlement mismatches, root-cause detection across large transaction volumes, and intelligent alerting for ops and merchant teams.
• Instrument data quality and health monitoring so that silent failures — missing records, schema shifts, delayed ingestion — are caught before they reach the merchant.
• Build APIs and tooling that enable PortOne's ops and merchant success teams to investigate, verify, and close financial discrepancies faster and with more confidence.
• Expand platform coverage by integrating new marketplaces and new report types, working closely with data formats that are often inconsistent, undocumented, or changing without notice.
• Uphold rigorous engineering standards — correctness in financial data is not negotiable, and you treat edge cases and off-by-one errors with the same seriousness as a production incident.
• Uphold high engineering standards across codebases and processes.
• Collaborate with product, design, infrastructure, and operations stakeholders.

Skills and Experience

* Have ideally 2 to 4 Years of experience shipping high quality products/live features and workflows

* Strong backend engineering foundation — Go (Preferred), Python, or equivalent; REST/gRPC APIs; database design.

* Understands how to build scalable, resilient, and observable distributed systems.

* Must have built data flows and applications end to end taking full ownership

Preferred Skills and Background

*Prior experience/built apps in golang backend

*Data and data engineering background — comfortable with data pipelines, ETL/ELT patterns, event-driven architectures, reconciliation logic, or analytical workloads.

*AI-native development — you build products where AI is a first-class component, not a bolt-on.

*AI agentic development — experience building or working with agent frameworks, tool-use patterns, LLM orchestration, or automated reasoning pipelines.

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

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

Your Experience at a Glance

We’re hiring a Data Scientist for our client delivers advanced data, analytics, and digital transformation solutions to help organizations modernize and drive business insights.

As a Data Scientist, you will play a key role in developing and deploying demand forecasting and pricing models, leveraging advanced statistical and machine learning techniques. You will collaborate closely with data engineers and business stakeholders to extract, transform, and analyze large datasets, ensuring robust and scalable solutions. This position requires strong ownership of model development, from data pipeline integration to model evaluation and reporting. Your work will directly impact business decision-making and operational efficiency, contributing to KPIP’s mission of enabling data-driven transformation.

KPIP is a global consulting and technology services firm specialising in data, analytics, and digital transformation. Serving a diverse range of industries, KPIP empowers organisations to modernize their data ecosystems and unlock actionable business insights. The company is recognized for its expertise in delivering scalable solutions, fostering a culture of innovation, and driving measurable impact for clients worldwide.

Key Responsibilities

● Develop and implement demand forecasting and pricing models using advanced statistical and machine learning techniques.

● Extract, transform, and analyze large datasets using Python and SQL to support model development and business insights.

● Collaborate with data engineers to build and maintain robust, scalable data pipelines for model training and inference.

● Apply regression, classification, time-series forecasting, ensemble methods, and feature engineering to solve business problems.

● Work with business stakeholders to understand requirements and translate them into actionable data science solutions.

● Create automated reports and dashboards to present and track model outputs and performance.

● Continuously evaluate and improve model accuracy and effectiveness based on business feedback and new data.

● Document methodologies, processes, and results to ensure transparency and reproducibility.

● Stay updated with the latest advancements in data science and machine learning to drive innovation within the team.

Required Skills

● Proven experience in demand forecasting and predictive modeling.

● Strong proficiency in Python, including pandas, NumPy, scikit-learn, and TensorFlow or PyTorch.

● Expertise in SQL for data extraction and transformation.

● Solid understanding of statistical and machine learning techniques such as regression, classification, time-series forecasting, ensemble methods, and feature engineering.

● Ability to analyze and interpret large, complex datasets to generate actionable insights.

● Experience collaborating with data engineers to develop scalable data pipelines.

● Strong problem-solving skills and attention to detail.

● Excellent communication skills for presenting technical concepts to non-technical stakeholders.

Nice to Have

● Experience with customer segmentation, recommendation systems, and sentiment analysis.

● Knowledge of inventory optimization, promotion uplift modeling, and campaign analysis.

● Familiarity with churn prediction models.

● Proficiency in Power BI for creating automated reports and dashboards.

● Experience in developing and maintaining data pipelines for model training and inference.

Why Join?

Join to work on impactful data science projects that drive real business outcomes and innovation. You’ll tackle complex technical challenges, collaborate with talented professionals, and have opportunities for continuous learning and growth, fosters a culture of collaboration, excellence, and data-driven decision-making, empowering you to make a meaningful difference in a dynamic environment.

About the Employment Model

Direct Hire (Client Payroll) : For this role, you’ll be hired directly by the client and be part of their internal team. Straatix supports the hiring process, but your employment, payroll, and benefits are all managed by the client.

Skill Set

Large language,Artificial Intelligence,Machine Learning

- 4–7 years of experience in software engineering/AI roles

- Strong programming skills in Python or TypeScript (Java/Go is a plus)

- Hands-on experience with LLMs, RAG pipelines, and AI frameworks

- Experience building APIs and working with distributed systems

- Familiarity with Kubernetes, Docker, and CI/CD pipelines

- Experience with cloud platforms (AWS/Azure/GCP)

Excellent communication

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

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.