Python
PostgreSQL
Machine Learning (ML)Full-Stack Machine Learning Engineer
Role: Full-Time, Long-Term Required: Python Preferred: C++
OVERVIEW
We are seeking a versatile ML engineer to join as a core member of our technical team. This is a long-term position for someone who wants to build sophisticated production systems and grow with a small, focused team. You will work across the entire stack—from data ingestion and feature engineering through model training, validation, and deployment.
The ideal candidate combines strong software engineering fundamentals with deep ML expertise, particularly in time series forecasting and quantitative applications. You should be comfortable operating independently, making architectural decisions, and owning systems end-to-end.
CORE TECHNICAL REQUIREMENTS
Python (Required): Professional-level proficiency writing clean, production-grade code—not just notebooks. Deep understanding of NumPy, Pandas, and their performance characteristics. You know when to use vectorized operations, understand memory management for large datasets, and can profile and optimize bottlenecks. Experience with async programming and multiprocessing is valuable.
Machine Learning (Required): Hands-on experience building and deploying ML systems in production. This goes beyond training models—you understand the full lifecycle: data validation, feature engineering, model selection, hyperparameter optimization, validation strategies, monitoring, and maintenance.
Specific experience we value: gradient boosting frameworks (LightGBM, XGBoost, CatBoost), time series forecasting, probabilistic prediction and uncertainty quantification, feature selection and dimensionality reduction, cross-validation strategies for non-IID data, model calibration.
You should understand overfitting deeply—not just as a concept but as something you actively defend against through proper validation, regularization, and architectural choices.
Data Pipelines (Required): Design and implement robust pipelines handling real-world messiness: missing data, late arrivals, schema changes, upstream failures. You understand idempotency, exactly-once semantics, and backfill strategies. Experience with workflow orchestration (Airflow, Prefect, Dagster) expected. Comfortable with ETL/ELT patterns, incremental vs full recomputation, data quality monitoring, database design and query optimization (PostgreSQL preferred), time series data at scale.
C++ (Preferred): Experience valuable for performance-critical components. Writing efficient C++ and interfacing with Python (pybind11, Cython) is a significant advantage.
HIGHLY DESIRABLE: MULTI-AGENT ORCHESTRATION
We are building systems leveraging LLM-based automation. Experience with multi-agent frameworks highly desirable: LangChain, LangGraph, or similar agent frameworks; designing reliable AI pipelines with error handling and fallbacks; prompt engineering and output parsing; managing context and state across agent interactions. You do not need to be an expert, but genuine interest and hands-on experience will set you apart.
DOMAIN EXPERIENCE: FINANCIAL DATA AND CRYPTO
Preference for candidates with experience in quantitative finance, algorithmic trading, or fintech; cryptocurrency markets and their unique characteristics; financial time series data and forecasting systems; market microstructure, volatility, and regime dynamics. This helps you understand why reproducibility is non-negotiable, why validation must account for temporal structure, and why production reliability cannot be an afterthought.
ENGINEERING STANDARDS
Code Quality: Readable, maintainable code others can modify. Proper version control (meaningful commits, branches, code review). Testing where appropriate. Documentation: docstrings, READMEs, decision records.
Production Mindset: Think about failure modes before they happen. Build in observability: logging, metrics, alerting. Design for reproducibility—same inputs produce same outputs.
Systems Thinking: Consider component interactions, not just isolated behavior. Understand tradeoffs: speed vs accuracy, flexibility vs simplicity. Zoom between architecture and implementation.
WHAT WE ARE LOOKING FOR
Self-Direction: Given a problem and context, you break it down, identify the path forward, and execute. You ask questions when genuinely blocked, not when you could find the answer yourself.
Long-Term Orientation: You think in years, not months. You make decisions considering future maintainability.
Intellectual Honesty: You acknowledge uncertainty and distinguish between what you know versus guess. When something fails, you dig into why.
Communication: You explain complex concepts clearly and document your reasoning.
EDUCATION
University degree in a quantitative/technical field preferred: Computer Science, Mathematics, Statistics, Physics, Engineering. Equivalent demonstrated expertise through work also considered.
TO APPLY
Include: (1) CV/resume, (2) Brief description of a production ML system you built, (3) Links to relevant work if available, (4) Availability and timezone.
Shubham Vishwakarma
Full Stack Developer - Averlon
About Capital Squared
About
Company social profiles
Similar jobs
PythonMachine Learning (ML)Job Description – AI Tech Lead
Location: Bangaluru
Experience: 10+ Years
Function: AI Center of Excellence (CoE)
Reporting To: Senior Vice President – CX / Head of AI CoE
We are seeking two highly experienced AI Tech Leads (AVP/DGM level) to drive the architecture, development, and delivery of large‑scale AI solutions spanning Predictive AI, GenAI, and Agentic AI across BPM, IT Services, Digital, Data Engineering, and Enterprise Transformation programs.
The role demands strong technical leadership, solution design capabilities, hands‑on execution ownership, and the ability to lead multi‑disciplinary teams to deliver scalable, production‑grade AI systems.
2. Key Responsibilities
A. Solution Architecture & Strategy
- Lead end‑to‑end solution architecture across Predictive AI, GenAI, Agentic AI, and enterprise data ecosystems.
- Partner with business and technology teams to define AI strategy, technical roadmaps, and implementation frameworks.
- Translate business goals into scalable AI architectures leveraging microservices, distributed systems, and modern AI toolchains.
- Own architectural decisions on model design, data pipelines, deployment frameworks, MLOps stack, and scaling strategies.
B. Project Delivery & Execution Leadership
- Drive the complete AI project lifecycle: Requirement Analysis → Architecture → Model Development → Engineering → Deployment → Monitoring.
- Lead AI engineering teams in developing production‑grade ML/GenAI/Agentic solutions with high reliability and performance.
- Establish and enforce engineering best practices, coding standards, DevOps/MLOps processes, and quality controls.
- Manage multiple concurrent AI initiatives with strong governance, risk mitigation, and stakeholder communication.
C. Technical Hands-on Expertise
- Architect and build complex AI systems involving:
- Large Language Models (LLMs) & GenAI apps
- Agentic workflows and autonomous task orchestration
- Predictive modeling, forecasting, optimization, and statistical modeling
- Knowledge graphs, vector databases, embeddings
- Data engineering pipelines (ETL/ELT) and cloud-native architectures
- Drive model evaluation, experimentation, benchmarking, A/B testing, and continuous improvements.
D. Team Leadership & Mentoring
- Lead and mentor a team of AI engineers, data scientists, MLOps engineers and developers.
- Build internal capabilities by establishing training, code reviews, reusable accelerators, and technical playbooks.
- Actively collaborate with product managers, data engineering teams, CX strategy teams, and domain SMEs.
E. Stakeholder & Client Management
- Act as a technology partner during client discussions, proposals, RFP responses, and solution demonstrations.
- Communicate complex AI concepts to CXOs, business leaders, and non-technical stakeholders seamlessly.
- Support pre-sales with solutioning, effort estimation, and technical presentations.
3. A. Technical Skills
- Strong proficiency in Python, cloud platforms (Azure/AWS/GCP), and AI frameworks (TensorFlow, PyTorch, LangChain, LlamaIndex).
- Hands-on experience building applications using:
- LLMs, RAG, fine‑tuning, prompt engineering
- Autonomous AI agents & multi-agent systems
- Predictive ML models (Regression, Classification, Clustering, NLP, CV)
- Expertise in microservices architecture, API design, scalable deployments.
- Strong command over SDLC, Agile methodologies, CI/CD, DevOps & MLOps.
- Experience with data engineering tools: Spark, Databricks, Airflow, Kafka, SQL/NoSQL, and modern data lakehouse platforms.
B. Functional & Domain Skills
- Experience working in BPM, Customer Experience, Digital Transformation, IT Services.
- Ability to map AI use cases to business value: workflow optimization, automation, customer experience, operations, and analytics.
C. Leadership & Soft Skills
- Strong team leadership and mentoring experience.
- Excellent communication, client-facing abilities, and stakeholder management skills.
- Strong decision-making, problem-solving, and delivery ownership.
4. Qualifications
- Bachelor’s / Master’s in Computer Science, Engineering, Data Science, or related fields.
- 10–15 years total experience with at least 5+ years leading AI/ML projects.
- Demonstrated success delivering large-scale AI programs in enterprise environments.
- Certifications in AI/ML, cloud, or architecture (preferred).
PythonMachine Learning (ML)Python Developer (Performance Optimization Focus)
Experience: 3–5 Years
Location: Remote (India-based candidates only)
Employment Type: Full-time
Role Overview
We are seeking a Python Developer with a strong focus on performance optimization and system efficiency. In this role, you will identify bottlenecks, enhance system performance, and contribute to building scalable, high-performance applications in a Linux-based environment.
Key Responsibilities
- Analyze and troubleshoot performance bottlenecks in applications and systems
- Optimize code, database queries, and architecture for scalability and speed
- Design, develop, test, and maintain robust Python applications
- Work with large datasets and improve data processing efficiency
- Collaborate with cross-functional teams to improve system reliability and performance
- Monitor system performance and implement proactive improvements
- Write clean, maintainable, and efficient code following best practices
Required Skills & Qualifications
- 3–5 years of hands-on experience in Python development
- Strong expertise in performance tuning and optimization techniques
- Experience with debugging and profiling tools
- Solid understanding of data structures and algorithms
- Experience with REST APIs and backend development
- Strong analytical and problem-solving skills
Linux & System Knowledge (Must-Have)
- Comfortable working in Linux/Unix environments
- Command-line proficiency, including:
- File editing (vi, nano)
- File permissions (chmod, chown)
- File downloads (wget, curl)
- Basic file and directory operations
Basic Python Knowledge (Interview Scope)
- Writing simple scripts and reusable functions
- String manipulation and data handling
- Example task: Count words in a file/string efficiently
Good to Have
- Familiarity with AI/ML concepts or tools
- Experience optimizing data-intensive or distributed systems
- Exposure to cloud platforms (AWS, GCP, Azure)
Why Join Us
- Work on performance-critical systems with real-world impact
- Fully remote work environment
- Opportunity to work with modern, scalable technologies
- Collaborative, growth-focused team culture
PythonMachine Learning (ML)
Amazon Web Services (AWS)Title: Senior Software Engineer – Python (Remote: Africa, India, Portugal)
Experience: 9 to 12 Years
INR : 40 LPA - 50 LPA
Location Requirement: Candidates must be based in Africa, India, or Portugal. Applicants outside these regions will not be considered.
Must-Have Qualifications:
- 8+ years in software development with expertise in Python
- kubernetes is important
- Strong understanding of async frameworks (e.g., asyncio)
- Experience with FastAPI, Flask, or Django for microservices
- Proficiency with Docker and Kubernetes/AWS ECS
- Familiarity with AWS, Azure, or GCP and IaC tools (CDK, Terraform)
- Knowledge of SQL and NoSQL databases (PostgreSQL, Cassandra, DynamoDB)
- Exposure to GenAI tools and LLM APIs (e.g., LangChain)
- CI/CD and DevOps best practices
- Strong communication and mentorship skills
PythonMachine Learning (ML)Level of skills and experience:
5 years of hands-on experience in using Python, Spark,Sql.
Experienced in AWS Cloud usage and management.
Experience with Databricks (Lakehouse, ML, Unity Catalog, MLflow).
Experience using various ML models and frameworks such as XGBoost, Lightgbm, Torch.
Experience with orchestrators such as Airflow and Kubeflow.
Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Fundamental understanding of Parquet, Delta Lake and other data file formats.
Proficiency on an IaC tool such as Terraform, CDK or CloudFormation.
Strong written and verbal English communication skill and proficient in communication with non-technical stakeholderst
DjangoPythonYour responsibilities as a backend engineer will include:
- Back-end software development
- Software engineering and designing data models and write effective APIs
- Working together with engineers and product teams
- Understanding business use cases and requirements for different internal teams
- Maintenance of existing projects and New feature development
- Consume and integrate classifier/ ML snippets from Data science team
What we are looking for:
- 2+ years of industry experience with the Python and Django framework.
- Degree in Computer Science or related field
- Good analytical skills with strong fundamentals of data structures and algorithms
- Experience building backend services with hands-on experience through all stages of Agile software development life cycle.
- Ability to write optimized codes,debug programs, and integrate applications with third party tools by developing various APIs
- Experience with Databases.
- Experience with writing REST-APIs.
- Prototyping initial collection and leveraging existing tools and/or creating new tools
- Experience working different types of datasets (e.g. unstructured, semi-structured, with missing information)
- Ability to think critically and creatively in a dynamic environment, while picking up new tools and domain knowledge along the way
- A positive attitude, and a growth mindset
Bonus:
- Experience with relevant Python libraries such as Requests, sklearn, Selenium
- Hands on experience in Machine learning implementations
- Experience with Cloud infrastructure (e.g. AWS) and relevant microservices
- Good Humor
PythonDjangoAmazon Web Services (AWS)PostgreSQL- Writing efficient, reusable, testable, and scalable code
- Understanding, analyzing, and implementing – Business needs, feature modification requests, conversion into software components
- Integration of user-oriented elements into different applications, data storage solutions
- Developing – Backend components to enhance performance and receptiveness, server-side logic, and platform, statistical learning models, highly responsive web applications
- Designing and implementing – High availability and low latency applications, data protection and security features
- Performance tuning and automation of application
- Working with Python libraries like Pandas, NumPy, etc.
- Creating predictive models for AI and ML-based features
- Keeping abreast with the latest technology and trends
- Fine-tune and develop AI/ML-based algorithms based on results
Technical Skills-
Good proficiency in,
- Python frameworks like Django, etc.
- Web frameworks and RESTful APIs
- Core Python fundamentals and programming
- Code packaging, release, and deployment
- Database knowledge
- Circles, conditional and control statements
- Object-relational mapping
- Code versioning tools like Git, Bitbucket
Fundamental understanding of,
- Front-end technologies like JS, CSS3 and HTML5
- AI, ML, Deep Learning, Version Control, Neural networking
- Data visualization, statistics, data analytics
- Design principles that are executable for a scalable app
- Creating predictive models
- Libraries like Tensorflow, Scikit-learn, etc
- Multi-process architecture
- Basic knowledge about Object Relational Mapper libraries
- Ability to integrate databases and various data sources into a unified system
- Basic knowledge about Object Relational Mapper libraries
- Ability to integrate databases and various data sources into a unified system
PythonMachine Learning (ML)
Data ScienceResponsibilities:
- Writing reusable, testable, and efficient code
- Design and implementation of low-latency, high-availability, and performant applications
- Integration of user-facing elements developed by front-end developers with server side logic
- Implementation of security and data protection
- Integration of data storage solutions (may include databases, key-value stores, blob stores, etc.)
- Expert in Python, with knowledge of at least one Python web framework (such as Django, Flask, etc depending on your technology stack)
- Familiarity with some ORM (Object Relational Mapper) libraries
- Able to integrate multiple data sources and databases into one system
- Understanding of the threading limitations of Python, and multi-process architecture
- Good understanding of server-side templating languages (such as Jinja 2, Mako, etc depending on your technology stack)
- Basic understanding of front-end technologies, such as JavaScript, HTML5, and CSS3
- Understanding of accessibility and security compliance (depending on the specific project)
- Knowledge of user authentication and authorization between multiple systems, servers, and environments
- Understanding of fundamental design principles behind a scalable application
- Familiarity with event-driven programming in Python
- Understanding of the differences between multiple delivery platforms, such as mobile vs desktop, and optimizing output to match the specific platform
- Able to create database schemas that represent and support business processes
- Strong unit test and debugging skills
- Basic knowledge of machine learning algorithm and libraries like keras, tensorflow, sklearn.
DjangoPythonMachine Learning (ML)Machine Learning (ML)
NodeJS (Node.js)
PHP
Machine Learning (ML)
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon