Technical Project Manager

Job description

Draughtsman

Location -Thane mumbai

Offer salary 8Lpa to 9 lpa

Qualification graduate

Mandatory knowledge - proficiency in AutoCAD , knowledge of Revit/BIM is an added advantage and MEP drawing

About Us:

Tradelab Technologies Pvt Ltd is not for those seeking comfort—we are for those hungry to make a mark in the trading and fintech industry. If you are looking for just another backend role, this isn’t it. We want risk-takers, relentless learners, and those who find joy in pushing their limits

every day. If you thrive in high-stakes environments and have a deep passion for performance driven backend systems, we want you.

What We Expect:

• You should already be exceptional at Backend. If you need hand-holding, this isn’t the place for you.

• You thrive on challenges, not on perks or financial rewards.

• You measure success by your own growth, not external validation.

• Taking calculated risks excites you—you’re here to build, break, and learn.

• You don’t clock in for a pay check; you clock in to outperform yourself in a high-frequency trading

environment.

• You understand the stakes—milliseconds can make or break trades, and precision is everything.

What You Will Do:

• Develop and optimize high-performance backend systems in for trading platforms and financial services.

• Architect low-latency, high-throughput microservices that push the boundaries of speed and efficiency.

• Build event-driven, fault-tolerant systems that can handle massive real-time data streams.

• Own your work—no babysitting, no micromanagement.

• Work alongside equally driven engineers who expect nothing less than brilliance.

• Learn faster than you ever thought possible.

Must-Have Skills:

• Proven expertise in Backend (if you need to prove yourself, this isn’t the role for you).

• Deep understanding of concurrency, memory management, and system design.

• Experience with Trading, market data processing, or low-latency systems.

• Strong knowledge of distributed systems, message queues (Kafka, RabbitMQ), and real-time processing.

• Hands-on with Docker, Kubernetes, and CI/CD pipelines.

• A portfolio of work that speaks louder than a resume.

Nice-to-Have Skills:

• Past experience in fintech, trading systems, or algorithmic trading.

• Contributions to open-source projects.

• A history of building something impactful from scratch.

• Understanding of FIX protocol, WebSockets, and streaming APIs.

Why Join Us?

• Work with a team that expects and delivers excellence.

• A culture where risk-taking is rewarded, and complacency is not.

• Limitless opportunities for growth—if you can handle the pace.

• A place where learning is currency, and outperformance is the only metric that matters.

• The opportunity to build systems that move markets, execute trades in microseconds, and redefine fintech.

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.

·        Monitoring the entire customer service process.(US Process)

·        Resolving customer issues brought to your attention.

·        Creating procedures and policies for effective customer service.

·        Planning how to train customer support professionals.

·        Standardizing the customer service delivery of an organization

·        Tracking the work of every customer service representative in the team.

·        Performing quality assurance surveys with customers.

·        Conveying customers’ feedback to the team.

·        Fixing appointments according to the convenience of clients and customers.

·        Interacting with customers to determine whether they have a desirable and shareable experience.

·        Helping every customer service professional improve.

·        Possessing the knowledge and ability to improve customer service of the organization.

·        Creating a pleasant work environment for customer service professionals.

Customer Service Executive Requirements

·        A minimum of 0 to 1.5 year of experience as a customer service representative.

·        Superior written and oral communication skills.

·        Leadership skills to guide and improve a team.

·        Data entry and basic computer skills.

·        Knowledge about customer relationship management systems.

·        Knowledge about the best mediation and conflict resolution tactics.

·        Ability to empathize with customers.

·        Utmost patience while dealing with angry and agitated customers.

·        Ability to respect every customer.

·        Fast and efficient problem-solving skills.

·        English should be fluent

Our client belongs to a reputed 125-year-old group of businesses in multiple locations with a turnover of $4 billion. It is one of the fastest growing brands in the packaged food category committed to adhering to the highest health and safety standards.

It has a diversified range of products in various categories like dairy and snacks. It is also a supplier to the leading institutional buyers like Mother Dairy, Walmart, Grofers, Spencers, Amazon and Big Basket.

The group has earned the recognition of "A Great Place to Work at" and the “Best Employer” tag as well.

• Managing budgets and campaigns across all digital channels to drive strong return on investment and efficient CAC
• Ensuring successful planning, execution, optimization for key traffic KPIs via paid, organic & own media channels
• Working closely with the management to share funnel conversion improvement ideas, feedback & present results
• Driving the sales performance of all the 3rd party e-commerce platforms & market places
• Overseeing or directly managing digital marketing channels across PPC, SEO, display, affiliates and email marketing and social media
• Managing all online activity in relation to traffic acquisition, sales, conversion and a/b testing and reporting
• Working with developers to improve website performance and overseeing website uploads and landing pages development
• Analysing various data in order to deliver data driven strategies in order to deliver top performance and achieve predesigned KPIs
• Working closely with demand planning and warehousing teams in order to ensure smooth order fulfilment

Desired Candidate Profile

• A degree in Marketing, Business Administration
• Prior experience in a similar role for at least 4 years in performance marketing in consumer products
• A solid understanding of e-commerce frameworks
• A self-starter mentality with the ability to manage multiple projects in a fast-paced working environment
• A high level of attention to detail including proven ability to manage multiple, competing priorities simultaneously
• Excellent communication skills