Lead ML Engineer, Data Science & Engineering

Job Description

Title: Lead ML Engineer, Data Science & Engineering

Company Name: bKash Ltd.

Vacancy: 1

Age: Na

Job Location: Dhaka

Salary: Negotiable

Experience:

• At least 5 years
• The applicants should have experience in the following business area(s): Banks, IT Enabled Service, Financial Technology (Fintech) Startup

• Bachelor of Science (BSc) in Computer Science & Engineering

• At least 5 years
• The applicants should have experience in the following business area(s): Banks, IT Enabled Service, Financial Technology (Fintech) Startup

Academic:

• M.Sc is preferred

Technical/Job Knowledge:

• Excellent command over C / C++ / Java / Python

• Strong understanding of high-performance and parallel computation

• Expert knowledge of ML frameworks: TensorFlow, PyTorch, Scikit-Learn, PyMC

• Hands-on experience with inference frameworks: ONNX Runtime, OpenVINO, TensorRT

• Strong proficiency with profiling tools: NVIDIA Nsight Compute, gProfiler, similar tool sets

• Solid understanding of ML system design, model governance, and MLOps concepts

Other Skills:

• Highly motivated, team player and must have the capacity to work under pressure

Job Purpose:

The Lead Machine Learning Engineer is responsible for end-to-end ownership of machine learning solutions, from technical design and model development to production deployment, scalability, and long-term sustainability.

This role goes beyond individual contribution and focuses on technical leadership, architecture decision-making, and mentoring senior and junior ML engineers. The Lead ML Engineer ensures that machine learning systems are robust, scalable, production-ready, and aligned with both business strategy and engineering best practices.

The role works closely with data scientists, software engineers, platform teams, and product stakeholders to translate complex business problems into reliable, high-impact ML systems deployed at scale.

Job Description:

Technical Leadership & Architecture

• Own the technical architecture of machine learning systems across multiple projects.

• Define modeling approaches, feature strategies, evaluation frameworks, anddeployment patterns.

• Review and approve ML design documents, code, and production pipelines.

Model Development & Advanced Analytics

• Design and implement state-of-the-art machine learning models using appropriatealgorithms and frameworks.

• Lead development of complex models requiring advanced optimization, large-scaledata processing, or real-time inference.

• Ensure model interpretability, robustness, and compliance where applicable.

Data Engineering & Feature Strategy

• Guide large-scale data preprocessing, feature engineering, and transformationpipelines.

• Ensure data quality, reproducibility, and performance efficiency.

• Collaborate with Big Data and Data Warehouse teams to align upstream data design.

Model Evaluation, Optimization & Governance

• Define standard evaluation metrics, validation strategies, and monitoring KPIs.

• Lead hyperparameter tuning, stress testing, bias detection, and performance benchmarking.

• Establish best practices for model versioning, retraining, and rollback strategies.

Deployment, Scaling & MLOps

• Lead deployment of ML models into production environments.

• Ensure models are scalable, fault-tolerant, and latency-optimized.

• Collaborate with MLOps and platform teams to standardize CI/CD, inference pipelines, and monitoring.

People Mentorship & Knowledge Leadership

• Mentor and guide Senior ML Engineers and ML Engineers.

• Conduct technical reviews, pair programming, and design walkthroughs.

• Raise overall team maturity through best practices, documentation, and internal knowledge sharing.

Cross-Functional Collaboration

• Work with Product, Engineering, Risk, and Business teams to translate requirements into ML solutions.

• Act as the technical ML point-of-contact for complex initiatives.

• Communicate trade-offs, risks, and system limitations to non-technical stakeholders.

Research, Innovation & Continuous Improvement

• Stay current with latest ML research, tooling, and industry best practices.

• Evaluate and introduce new techniques, libraries, or frameworks where they add value.

• Drive experimentation and innovation while maintaining production stability

Privacy Disclaimer and Consent:

By submitting your application, you consent that bKash may use, process, and retain your information for recruitment purposes, including for future opportunities. bKash is committed to protecting your personal information in accordance with applicable laws and regulations.