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About Us:

The QX Impact was launched with a mission to make A.I accessible and affordable and deliver AI Products/Solutions at scale for the enterprises by bringing the power of Data, AI, and Engineering to drive digital transformation. We believe without insights; businesses will continue to face challenges to better understand their customers and even lose them. Secondly, without insights businesses won't’ be able to deliver differentiated products/services; and finally, without insights, businesses can’t achieve a new level of “Operational Excellence” is crucial to remain competitive, meeting rising customer expectations, expanding markets, and digitalization.

Role Overview

We are seeking a Machine Learning Engineer to lead the end-to-end development of production-grade analytical applications. This is a high-impact role requiring a blend of deep statistical modeling and machine learning. You will be responsible transforming raw consolidated data into high-accuracy forecasts through advanced feature engineering, rigorous model selection, and statistical validation.

This role is for an engineer who thrives in the research-to-code transition, ensuring that every model is mathematically sound, resistant to overfitting, and optimized for high-dimensional manufacturing data.

Responsibilities:

• Feature Engineering & Discovery: Design and build complex feature sets for diverse problem types, including behavioural features for churn, sensor-based lags for maintenance, and seasonal encodings for demand forecasting.
• Model Selection & Optimization: Conduct systematic experimentation across diverse algorithms (e.g., XGBoost, LightGBM, Prophet, or Deep Learning) to identify the best-performing models.
• Model Training & Testing: Develop, train, tune, and test a variety of ML architectures including time-series, classification and regression.
• Statistical Validation & Evaluation: Define and track complex evaluation metrics tailored to manufacturing, such as MAPE, RMSE, etc., while performing deep-dive bias-variance analysis.
• EDA & Research: Perform exploratory data analysis on consolidated "Gold" layer data to uncover hidden drivers of business outcomes and identify correlations between external signals.
• Refinement & Performance Tuning: Address critical modeling challenges including bias-variance tradeoffs, class imbalance, and overfitting to ensure models generalize to real-world production data.

Skills & Requirements:

• 3+ Years of Experience: Proven track record of developing and delivering production-grade ML models across multiple domains (Sales, Finance, Manufacturing, or Supply Chain).
• Mastery of the Python Ecosystem: Expert-level skills in Pandas, NumPy, Scikit-learn, and SciPy.
• Advanced Algorithmic Knowledge: Deep expertise in supervised and unsupervised learning, specifically ensemble methods (Boosting/Bagging) and time-series frameworks.
• Statistical Foundations: Strong grasp of hypothesis testing, probability distributions, and the mathematical principles behind model evaluation and optimization.
• SQL Proficiency: Expert ability to manipulate data within consolidated database layers to create the "Silver" feature sets required for training.
• Education: Bachelor’s or Master’s degree in a quantitative field (e.g., Data Science, Statistics, Mathematics, or Computer Science).
• Cloud Awareness: Experience with Azure Machine Learning or similar cloud modelling environments.
• Engineering Familiarity: Basic understanding of Docker, MLflow, or FastAPI for handing models off to deployment teams.

Personal Attributes:

• Strong problem-solving skills with a passion for data architecture.
• Excellent communication skills with the ability to explain complex data concepts to non-technical stakeholders.
• Highly collaborative, capable of working with cross-functional teams.
• Ability to thrive in a fast-paced, agile environment while managing multiple priorities effectively.

Competencies:

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

Job Description:

1.     Machine Learning Development & Deployment

·      Design and implement supervised and unsupervised models for predictive analytics, including churn prediction, demand forecasting, renewal risk scoring, and cross sell/upsell opportunity identification.

·      Translate business problems into ML frameworks and production solutions that improve efficiency, revenue, or customer experience.

·      Build, optimize, and maintain ML pipelines using tools such as MLflow, Airflow, or Kubeflow.

2.     Cross-Functional ML Use Cases

·      Partner with teams across Sales (e.g., lead scoring, next-best action), Customer Service (e.g., case deflection, sentiment analysis), Finance (e.g., revenue forecasting, fraud detection), Supply Chain (e.g., inventory optimization, ETA prediction), and Order Fulfillment (e.g., delivery risk modeling) to define impactful ML use cases.

·      Develop domain-specific models and continuously improve them using feedback loops and real-world performance data. 3.

3.      Model Governance and MLOps

·      Ensure robust model monitoring, versioning, and retraining strategies to keep models reliable in dynamic environments.

·      Work closely with DevOps and Data Engineering teams to automate deployment, CI/CD workflows, and cloud-native ML infrastructure (AWS/GCP/Azure).

4. Data Engineering and Feature Architecture

·      Collaborate with data engineers to define feature stores, data quality checks, and model-ready datasets on platforms like Snowflake or Databricks.

·      Perform feature selection, transformation, and engineering aligned with each domain’s business logic. 5. Communication & Stakeholder Collaboration

·      Present technical insights and model results to business and executive stakeholders in a clear, actionable format.

·      Work with Product Owners and Program Managers to scope, prioritize, and plan delivery of ML projects.

Qualifications:

Required

• Bachelor’s or Master’s degree in (e.g., Computer Science, Engineering, Statistics, Mathematics)

• 4+ years of experience in machine learning, data science.

• Proficiency in Python, XGBoost, PyTorch, TensorFlow, or similar.

• Experience deploying models into production using ML pipelines and orchestration frameworks.

• Strong understanding of data structures, SQL, and cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).

• Hands-on experience in implementing machine learning algorithms such as Random Forest, XGBoost, Logistic Regression, and Deep Learning techniques including Neural Networks (ANN, CNN)

Preferred:

• Experience supporting business functions such as Finance, Sales, or Operations with ML use cases.

• Familiarity with MLOps tools (MLflow, SageMaker Pipelines, Feature Store).

• Exposure to enterprise data platforms (e.g., Snowflake, Oracle Fusion, Salesforce).

• Background in statistics, forecasting, optimization, or recommendation systems.

🎯 Ideal Candidate Profile:

This role requires a seasoned engineer/scientist with a strong academic background from a premier institution and significant hands-on experience in deep learning (specifically image processing) within a hardware or product manufacturing environment.

📋 Must-Have Requirements:

Experience & Education Combinations:

Candidates must meet one of the following criteria:

• Doctorate (PhD) + 2 years of related work experience
• Master's Degree + 5 years of related work experience
• Bachelor's Degree + 7 years of related work experience

Technical Skills:

• Minimum 5 years of hands-on experience in all of the following:
• Python
• Deep Learning (DL)
• Machine Learning (ML)
• Algorithm Development
• Image Processing
• 3.5 to 4 years of strong proficiency with PyTorch OR TensorFlow / Keras.

Industry & Institute:

• Education: Must be from a premier institute (IIT, IISC, IIIT, NIT, BITS) or a recognized regional tier 1 college.
• Industry: Current or past experience in a Product, Semiconductor, or Hardware Manufacturing company is mandatory.
• Preference: Candidates from engineering product companies are strongly preferred.

ℹ️ Additional Role Details:

• Interview Process: 3 technical rounds followed by 1 HR round.
• Work Model: Hybrid (requiring 3 days per week in the office).

Based on the job description you provided, here is a detailed breakdown of the Required Skills and Qualifications for this AI/ML/LLM role, formatted for clarity.

📝 Required Skills and Competencies:

💻 Programming & ML Prototyping:

• Strong Proficiency: Python, Data Structures, and Algorithms.
• Hands-on Experience: NumPy, Pandas, Scikit-learn (for ML prototyping).

🤖 Machine Learning Frameworks:

• Core Concepts: Solid understanding of:
• Supervised/Unsupervised Learning
• Regularization
• Feature Engineering
• Model Selection
• Cross-Validation
• Ensemble Methods: Experience with models like XGBoost and LightGBM.

🧠 Deep Learning Techniques:

• Frameworks: Proficiency with PyTorch OR TensorFlow / Keras.
• Architectures: Knowledge of:
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory networks (LSTMs)
• Transformers
• Attention Mechanisms
• Optimization: Familiarity with optimization techniques (e.g., Adam, SGD), Dropout, and Batch Normalization.

💬 LLMs & RAG (Retrieval-Augmented Generation):

• Hugging Face: Experience with the Transformers library (tokenizers, embeddings, model fine-tuning).
• Vector Databases: Familiarity with Milvus, FAISS, Pinecone, or ElasticSearch.
• Advanced Techniques: Proficiency in:
• Prompt Engineering
• Function/Tool Calling
• JSON Schema Outputs

🛠️ Data & Tools:

• Data Management: SQL fundamentals; exposure to data wrangling and pipelines.
• Tools: Experience with Git/GitHub, Jupyter, and basic Docker.

🎓 Minimum Qualifications (Experience & Education Combinations):

Candidates must have experience building AI systems/solutions with Machine Learning, Deep Learning, and LLMs, meeting one of the following criteria:

• Doctorate (Academic) Degree + 2 years of related work experience.
• Master's Level Degree + 5 years of related work experience.
• Bachelor's Level Degree + 7 years of related work experience.

⭐ Preferred Traits and Mindset:

• Academic Foundation: Solid academic background with strong applied ML/DL exposure.
• Curiosity: Eagerness to learn cutting-edge AI and willingness to experiment.
• Communication: Clear communicator who can explain ML/LLM trade-offs simply.
• Ownership: Strong problem-solving and ownership mindset.