Blockchain Data and ML Engineer at Deqode · Indore · 0 - 2 years · ₹6L - ₹12L / yr · Bootstrapped · Posted 2 Jul 2025
About Us
Alfred Capital - Alfred Capital is a next-generation on-chain proprietary quantitative trading technology provider, pioneering fully autonomous algorithmic systems that reshape trading and capital allocation in decentralized finance.
As a sister company of Deqode — a 400+ person blockchain innovation powerhouse — we operate at the cutting edge of quant research, distributed infrastructure, and high-frequency execution.
What We Build
- Alpha Discovery via On‑Chain Intelligence — Developing trading signals using blockchain data, CEX/DEX markets, and protocol mechanics.
- DeFi-Native Execution Agents — Automated systems that execute trades across decentralized platforms.
- ML-Augmented Infrastructure — Machine learning pipelines for real-time prediction, execution heuristics, and anomaly detection.
- High-Throughput Systems — Resilient, low-latency engines that operate 24/7 across EVM and non-EVM chains tuned for high-frequency trading (HFT) and real-time response
- Data-Driven MEV Analysis & Strategy — We analyze mempools, order flow, and validator behaviors to identify and capture MEV opportunities ethically—powering strategies that interact deeply with the mechanics of block production and inclusion.
Evaluation Process
- HR Discussion – A brief conversation to understand your motivation and alignment with the role.
- Initial Technical Interview – A quick round focused on fundamentals and problem-solving approach.
- Take-Home Assignment – Assesses research ability, learning agility, and structured thinking.
- Assignment Presentation – Deep-dive into your solution, design choices, and technical reasoning.
- Final Interview – A concluding round to explore your background, interests, and team fit in depth.
- Optional Interview – In specific cases, an additional round may be scheduled to clarify certain aspects or conduct further assessment before making a final decision.
Job Description : Blockchain Data & ML Engineer
As a Blockchain Data & ML Engineer, you’ll work on ingesting and modelling on-chain behaviour, building scalable data pipelines, and designing systems that support intelligent, autonomous market interaction.
What You’ll Work On
- Build and maintain ETL pipelines for ingesting and processing blockchain data.
- Assist in designing, training, and validating machine learning models for prediction and anomaly detection.
- Evaluate model performance, tune hyperparameters, and document experimental results.
- Develop monitoring tools to track model accuracy, data drift, and system health.
- Collaborate with infrastructure and execution teams to integrate ML components into production systems.
- Design and maintain databases and storage systems to efficiently manage large-scale datasets.
Ideal Traits
- Strong in data structures, algorithms, and core CS fundamentals.
- Proficiency in any programming language
- Familiarity with backend systems, APIs, and database design, along with a basic understanding of machine learning and blockchain fundamentals.
- Curiosity about how blockchain systems and crypto markets work under the hood.
- Self-motivated, eager to experiment and learn in a dynamic environment.
Bonus Points For
- Hands-on experience with pandas, numpy, scikit-learn, or PyTorch.
- Side projects involving automated ML workflows, ETL pipelines, or crypto protocols.
- Participation in hackathons or open-source contributions.
What You’ll Gain
- Cutting-Edge Tech Stack: You'll work on modern infrastructure and stay up to date with the latest trends in technology.
- Idea-Driven Culture: We welcome and encourage fresh ideas. Your input is valued, and you're empowered to make an impact from day one.
- Ownership & Autonomy: You’ll have end-to-end ownership of projects. We trust our team and give them the freedom to make meaningful decisions.
- Impact-Focused: Your work won’t be buried under bureaucracy. You’ll see it go live and make a difference in days, not quarters
What We Value:
- Craftsmanship over shortcuts: We appreciate engineers who take the time to understand the problem deeply and build durable solutions—not just quick fixes.
- Depth over haste: If you're the kind of person who enjoys going one level deeper to really "get" how something works, you'll thrive here.
- Invested mindset: We're looking for people who don't just punch tickets, but care about the long-term success of the systems they build.
- Curiosity with follow-through: We admire those who take the time to explore and validate new ideas, not just skim the surface.
Compensation:
- INR 6 - 12 LPA
- Performance Bonuses: Linked to contribution, delivery, and impact.
Shubham Vishwakarma
Full Stack Developer - Averlon
About Deqode
About
At Deqode, our purpose is to help businesses solve complex problems using new-age technologies. We provide enterprise blockchain solutions to businesses.
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Job Summary
We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate should have strong expertise in SQL, Python, Linux, and modern data engineering practices to support data integration, transformation, and analytics.
Key Responsibilities
- Design, develop, and maintain ETL/ELT data pipelines.
- Write efficient and optimized SQL queries for data extraction, transformation, and reporting.
- Develop automation scripts using Python for data processing and workflow optimization.
- Work with Linux environments for deployment, monitoring, and troubleshooting.
- Ensure data quality, integrity, and reliability across data platforms.
- Collaborate with data analysts, software engineers, and business stakeholders to deliver data solutions.
- Monitor, troubleshoot, and optimize data pipelines for performance and scalability.
- Implement best practices for data security, governance, and documentation.
Required Skills
- Strong experience in Data Engineering concepts and ETL/ELT processes.
- Proficiency in SQL, including query optimization and database design.
- Strong programming skills in Python.
- Hands-on experience with Linux commands, shell scripting, and system administration basics.
- Experience with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
- Familiarity with Git/version control.
- Strong analytical and problem-solving skills.
Preferred Skills
- Experience with cloud platforms (AWS, Azure, or GCP).
- Knowledge of Apache Spark, Airflow, Kafka, or similar data engineering tools.
- Experience with data warehousing solutions and big data technologies.
- Understanding of CI/CD pipelines and containerization (Docker/Kubernetes).
Qualifications
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
- Relevant certifications in cloud or data engineering are an added advantage.
Data Engineer Short Hiring Post
🚨 Hiring: Data Engineer
🔹 Experience: 5–9 Years
🔹 Location: Bangalore / Hyderabad
🔹 Skills: PySpark, Python, SQL, ETL, CI/CD, Data Modeling
🔹 Process: L1 Virtual → L2 F2F Karat Test
🔹 F2F: Bangalore / Hyderabad Location
🔹 Positions: Immediate requirement
⚠️ Note: Candidates must be available for F2F Karat immediately after L1.
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We are looking for a talented and driven Data Scientist to join our growing Analytics team in India. In this role, you will work at the intersection of advanced machine learning, scalable MLOps infrastructure, and domain-specific healthcare analytics. You will collaborate closely with cross-functional teams to build, deploy, and maintain production-grade ML models that drive real-world impact in clinical trials and healthcare operations.
KEY RESPONSIBILITIES
End-to-End ML Development
• Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.
• Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.
• Validate model performance using appropriate statistical techniques and domain knowledge.
MLOps & Production Deployment
• Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.
• Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.
• Ensure model reliability, observability, and performance in live production environments.
Language Models & LLM Applications
• Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.
• Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.
• Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.
• Support exploratory work around LLM integration and prompt engineering for internal tooling.
Domain-Driven Analytics
• Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.
• Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.
• Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.
REQUIRED QUALIFICATIONS
Education
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.
Experience
• 2–4 years of hands-on experience in a data science or machine learning role.
• Demonstrable experience deploying ML models in production environments (not just prototyping).
Technical Skills
• Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).
• Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.
• Hands-on experience with BERT-family models and Hugging Face Transformers library.
• Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.
• Solid understanding of SQL and working with large structured/unstructured datasets.
• Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).
GOOD TO HAVE
• Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).
• Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.
• Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.
• Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).
• Contributions to open-source ML projects or published research.
THIS ROLE IS NOT FOR YOU IF…
• You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.
• Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.
4 - 10 years of experience in designing and buildingarchitecting highly resilient data platforms
∙Strong knowledge of data engineering, architecture and data modeling
∙Experience in platforms like Databricks and Snowflake
∙Experience on building applications on cloud (AWS or Azure or Google Cloud)
∙Strong analytical and problem-solving skills
∙Prior experience in developing data or computation intensive (e.g. grid based) backend applications is an
advantage
∙OOP design skills with an understanding or at least personal interest towards the concepts of Functional
Programming
∙Willingness to understand and enhance other people’s code, being able to work in an environment where
developers will oversee and work on wider components also dealing with older “legacy” code
∙Strong programming skills (Java/ Scala / Python) skills with the willingness to pick up the other language if not
already mastered at a sufficient level is important
∙Spring knowledge is an advantage, but in general willingness to learn, work with and even enhance in-house
developed frameworks is a must
∙Prior experience in working with Git, Bitbucket, Jenkins, working with PR-s, using JIRA, following the Scrum Agile
methodology is an advantage
∙Prior knowledge of financial products is an advantage
∙Bachelors or Masters in any relevant field of IT/Engineering area is an advantage
We are looking for a talented and driven Data Scientist to join our growing Analytics team in India. In this role, you will work at the intersection of advanced machine learning, scalable MLOps infrastructure, and domain-specific healthcare analytics. You will collaborate closely with cross-functional teams to build, deploy, and maintain production-grade ML models that drive real-world impact in clinical trials and healthcare operations.
KEY RESPONSIBILITIES
End-to-End ML Development
• Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.
• Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.
• Validate model performance using appropriate statistical techniques and domain knowledge.
MLOps & Production Deployment
• Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.
• Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.
• Ensure model reliability, observability, and performance in live production environments.
Language Models & LLM Applications
• Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.
• Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.
• Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.
• Support exploratory work around LLM integration and prompt engineering for internal tooling.
Domain-Driven Analytics
• Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.
• Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.
• Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.
REQUIRED QUALIFICATIONS
Education
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.
Experience
• 2–4 years of hands-on experience in a data science or machine learning role.
• Demonstrable experience deploying ML models in production environments (not just prototyping).
Technical Skills
• Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).
• Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.
• Hands-on experience with BERT-family models and Hugging Face Transformers library.
• Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.
• Solid understanding of SQL and working with large structured/unstructured datasets.
• Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).
GOOD TO HAVE
• Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).
• Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.
• Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.
• Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).
• Contributions to open-source ML projects or published research.
THIS ROLE IS NOT FOR YOU IF…
• You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.
• Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Role Summary
We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:
What You'll Own
- Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
- AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
- Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
- Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
- AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
- ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
- APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
- Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.
Key Skills — Non-Negotiable (Must-Have, Strong Level)
- Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
- SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
- AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
- Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
- ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
- Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).
Key Skills — Good to Have
- Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
- FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
- ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
- Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
- Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.
Description
We’re seeking a highly skilled, execution-focused Senior Data Scientist with a minimum of 5 years of experience. This role demands hands-on expertise in building, deploying, and optimizing machine learning models at scale, while working with big data technologies and modern cloud platforms. You will be responsible for driving data-driven solutions from experimentation to production, leveraging advanced tools and frameworks across Python, SQL, Spark, and AWS. The role requires strong technical depth, problem-solving ability, and ownership in delivering business impact through data science.
Responsibilities
- Design, build, and deploy scalable machine learning models into production systems.
- Develop advanced analytics and predictive models using Python, SQL, and popular ML/DL frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch).
- Leverage Databricks, Apache Spark, and Hadoop for large-scale data processing and model training.
- Implement workflows and pipelines using Airflow and AWS EMR for automation and orchestration.
- Collaborate with engineering teams to integrate models into cloud-based applications on AWS.
- Optimize query performance, storage usage, and data pipelines for efficiency.
- Conduct end-to-end experiments, including data preprocessing, feature engineering, model training, validation, and deployment.
- Drive initiatives independently with high ownership and accountability.
- Stay up to date with industry best practices in machine learning, big data, and cloud-native deployments.
Requirements
- Minimum 5 years of experience in Data Science or Applied Machine Learning.
- Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
- Proven expertise in deploying ML models into production systems.
- Experience with big data platforms (Hadoop, Spark) and distributed data processing.
- Hands-on experience with Databricks, Airflow, and AWS EMR.
- Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.).
- Solid understanding of query optimization, storage systems, and data pipelines.
- Excellent problem-solving skills, with the ability to design scalable solutions.
- Strong communication and collaboration skills to work in cross-functional teams.
Benefits
- Best-in-class salary: We hire strong talent and compensate accordingly.
- Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
- Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
- High-impact work: Build AI-first systems and products used at scale by global clients.
About Us
Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.
Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Job Summary
We are seeking a motivated Data Engineer with strong skills in SQL, Python, and Linux to design, build, and maintain scalable data pipelines and support data-driven decision-making. The ideal candidate should have experience working with large datasets, ETL processes, and relational databases while ensuring data quality and performance.
Key Responsibilities
- Design, develop, and maintain ETL/ELT data pipelines.
- Write optimized SQL queries, stored procedures, and database objects.
- Develop Python scripts for data extraction, transformation, and automation.
- Work in Linux environments to manage scripts, cron jobs, and system processes.
- Monitor and troubleshoot data pipeline failures.
- Ensure data integrity, consistency, and quality across systems.
- Collaborate with data analysts, software engineers, and business stakeholders.
- Optimize database performance and query execution.
- Participate in code reviews and follow best engineering practices.
Required Skills
- Strong proficiency in SQL (joins, subqueries, window functions, CTEs, indexing, query optimization).
- Good programming experience in Python.
- Hands-on experience with Linux commands and shell scripting.
- Understanding of ETL/ELT concepts and data warehousing.
- Knowledge of relational databases such as PostgreSQL, MySQL, Oracle, or SQL Server.
- Familiarity with Git for version control.
- Strong problem-solving and analytical skills.
Skills Referential (Required knowledge, skills and abilities)
Technical Skills:
Python
Pyspark
SQL
ETL Aws, Azure, gcp
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.
Job Summary:
We are looking for a Senior Data Engineer who is creative, collaborative, and adaptable to join our agile team of data scientists, engineers, and UX developers. The role focuses on building and maintaining robust data pipelines to support advanced analytics, data science, and BI solutions.
As a Senior Data Engineer, you will work with internal and external data, collaborate with data scientists, and contribute to the design, development, and deployment of innovative solutions.
Key Responsibilities:
- Design, develop, test, and maintain optimal data pipeline and ETL architectures.
- Map out data systems and define/design required integrations, ETL, BI, and AI systems/processes.
- Prepare and optimize data for predictive and prescriptive modeling.
- Collaborate with teams to integrate ERP data into the enterprise data lake, ensuring seamless flow and quality.
- Enhance cloud data infrastructure on AWS or Azure for scalability and performance.
- Utilize big data tools and frameworks to optimize data acquisition and preparation.
- Build architectures to move data to/from data lakes and data warehouses for advanced analytics.
- Develop and curate data models for analytics, dashboards, and reports.
- Conduct code reviews, maintain production-level code, and implement testing approaches.
- Monitor, troubleshoot, and resolve data ingestion workflows to maintain reliability and uptime.
- Drive innovation and implement efficient new approaches to data engineering tasks.
Must-Have Skills:
- Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field.
- 5+ years of experience working with enterprise data platforms, including building and managing data lakes.
- 3–5 years of experience designing and implementing data warehouse solutions.
- Expertise in SQL, including developing stored procedures (SP) and applying advanced data design concepts.
- Proficiency in Spark (Python/Scala) and Spark Streaming for real-time data pipelines.
- Experience with AWS or Azure services (e.g., AWS Glue, Azure Data Factory, Redshift, Snowflake).
- Familiarity with big data tools such as Apache Kafka, Apache Spark, or Flink.
- Hands-on experience with orchestration tools (e.g., Apache Airflow, Prefect).
- Knowledge of CI/CD processes, version control (e.g., Git, Jenkins), and deployment automation.
- Strong problem-solving, communication, and collaboration skills.
Good-to-Have Skills:
- Experience in integrating ERP data into data lakes.
- Experience with traditional ETL tools (e.g., Talend, Pentaho).
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!
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Full Stack Developer - Averlon