Qureos Mentor

Soothsayer Analytics

<div><div><div><p>Working Hours : <b>Full Time</b></p>
<p>Locations : <b>Hyderabad</b></p>
<p>Experience : <b>8&ndash;10 years</b></p><p><br>
</p></div>
</div><b>About the Role:
</b><p>Soothsayer Analytics is a global AI &amp; Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning &amp; GenAI solutions&mdash;spanning predictive analytics, optimization, NLP, and AI-driven platforms&mdash;that help leading enterprises forecast, automate, and gain a competitive edge.
</p><p>Behind these innovations lies robust, secure, and scalable cloud infrastructure. As part of our Cloud Engineering team, you&rsquo;ll help design and operate next-gen cloud systems that power cutting-edge AI solutions.</p>
<p><b>Job Overview</b></p>
<p>
We seek a <b>Senior Cloud Engineer</b> to design, build, and optimize scalable, secure, and cost-efficient cloud environments. You&rsquo;ll collaborate with AI/ML teams to deliver production-grade systems, automate deployments, and ensure resilience of data pipelines, APIs, and AI services across AWS, Azure, and GCP. This is a hands-on role where cloud architecture meets engineering excellence.
</p><p><b>Key Responsibilities</b></p>
<p><b>Cloud Architecture &amp; Infrastructure</b></p>
<p>
&middot; Design and implement cloud-native solutions on AWS, Azure, or GCP.
</p>
<p>&middot; Build secure, highly available, and cost-optimized cloud infrastructure.
</p><p>&middot; Implement networking, IAM, security, and compliance best practices.
</p><p><b>Automation &amp; DevOps</b></p>
<p>
&middot; Develop Infrastructure as Code (IaC) using Terraform/CloudFormation.
</p><p>&middot; Implement CI/CD pipelines to automate deployments for AI/ML and data platforms.
</p>
<p>&middot; Enable monitoring, logging, and alerting using cloud-native or third-party tools.
</p>
<p><b>Containerization &amp; Orchestration
</b></p><p>&middot; Manage Kubernetes clusters and containerized workloads (Docker, EKS/AKS/GKE).
</p>
<p>&middot; Optimize workloads for scalability, performance, and cost efficiency.
</p>
<p><b>Collaboration &amp; Support</b></p>
<p>
&middot; Partner with Data &amp; AI teams to ensure cloud infra supports ML/LLM workloads (e.g., GPU provisioning, vector DB hosting).
</p>
<p>&middot; Troubleshoot complex production issues and optimize cloud operations.
</p>
<p>&middot; Mentor junior engineers on cloud best practices.
</p>
<p><b>Required Skills &amp; Qualifications</b></p>
<p><b>
Education:</b>Bachelor&rsquo;s/Master&rsquo;s in Computer Science, Cloud Computing, or related fields.</p>
<p><b>
Experience:</b>6&ndash;10 years in cloud engineering/DevOps with expertise in:
</p><p>&middot; <b>Cloud Platforms:</b> AWS, Azure, or GCP (multi-cloud experience preferred).
</p><p>&middot; <b>Infrastructure as Code:</b> Terraform, CloudFormation, Pulumi.
</p>
<p>&middot; <b>Containers &amp; Orchestration:</b> Docker, Kubernetes, Helm
</p><p>&middot; <b>CI/CD Tools:</b> Jenkins, GitHub Actions, GitLab CI, or Azure DevOps
</p>
<p>&middot; <b>Networking &amp; Security:</b> VPCs, IAM, firewalls, VPN, secrets management.
</p>
<p>&middot; <b>Observability:</b> Prometheus, Grafana, ELK, or cloud-native monitoring tools.
</p><p>&middot; <b>AI/ML Enablement (preferred):</b> GPU provisioning, supporting MLOps pipelines.
</p>
<p><b>Skills Matrix</b></p>
<p>
Candidates must submit a detailed resume and fill out the following matrix:</p>
<div>
<div><div><div><p><b>Skill</b></p><br>
</div><div><p><b>Details</b></p><br>
</div><div><p><b>Skills Last Used</b></p><br>
</div><div><p><b>Experience (months)</b></p><br>
</div><div><p><b>Self-Rating (0&ndash;10)</b></p><br>
</div></div><br>
<div><div><p>AWS / Azure / GCP</p><br>
</div></div><br>
<p></p><div><div><p>Terraform / IaC</p><br>
</div></div><br>
<p></p><div><div><p>Docker / Kubernetes</p><br>
</div></div><br>
<p></p><div><div><p>CI/CD (Jenkins, GitHub Actions, etc.)<br>
</p></div></div><br>
<p></p><div><div><p>Networking &amp; Security<br>
</p></div></div><br>
<p></p><div><div><p>Monitoring &amp; Logging<br>
</p></div></div><br>
<p></p><div><div><p>GPU / AI Workload Support<br>
</p></div></div><br>
<p></p><div><div><p>Gen AI Deployments<br>
</p></div></div><br>
</div></div><p></p><p><b>Instructions for Candidates:</b></p>
<p>
&middot; Provide a detailed resume highlighting cloud projects (infrastructure automation, containerization, multi-cloud deployments, AI/ML workload support).
</p>
<p>&middot; Fill out the above skills matrix with accurate dates, duration, and self-ratings.</p></div><p></p>

Senior Cloud Engineer

<div><div><div><p>Working Hours : <b>Full-time
</b></p>
<p>Locations : <b>Hyderabad</b></p>
<p>Experience : <b>Min 2 years</b></p><p><br>
</p></div>
</div><b>About the Role:
</b><p>Soothsayer Analytics is a global AI &amp; Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning &amp; GenAI solutions&mdash;spanning predictive analytics, optimization, NLP, computer vision, and enterprisescale AI platforms&mdash;that help leading enterprises forecast, automate, and gain a competitive edge.
</p><p>This is a unique opportunity to begin your AI career by working on cutting-edge GenAI, ML, and datadriven projects across industries.</p>
<p><b>Job Overview</b></p>
<p>
We seek a <b>Junior AI Engineer</b> with min 2years of hands-on experience (or strong internships/projects) in machine learning, NLP, or GenAI. You will support the development and deployment of AI models, contribute to data preparation pipelines, and learn best practices in MLOps and applied AI engineering.
</p><p><b>Key Responsibilities</b></p>
<p><b>Model Development &amp; Support</b></p>
<p>
&middot; Assist in building ML models (classification, regression, clustering, forecasting).
</p>
<p>&middot; Contribute to fine-tuning and prompt engineering for LLMs (GPT, LLaMA, etc.).
</p><p>&middot; Experiment with vector databases and RAG pipelines for GenAI applications.
</p><p><b>Data Preparation &amp; Engineering</b></p>
<p>
&middot; Work with structured/unstructured datasets for ML training.
</p><p>&middot; Perform data cleaning, feature engineering, and basic pipeline development.
</p>
<p><b>MLOps &amp; Deployment (Learning Role)
</b></p><p>&middot; Support containerization and deployment of models using Docker/Kubernetes.
</p>
<p>&middot; Learn to implement CI/CD pipelines for ML workflows.
</p>
<p><b>Collaboration &amp; Learning</b></p>
<p>
&middot; Work under guidance of senior engineers and data scientists.
</p>
<p>&middot; Document experiments and present results to technical and business stakeholders.
</p>
<p><b>Required Skills &amp; Qualifications</b></p>
<p><b>
Education:</b> Bachelor&rsquo;s in Computer Science, AI, Data Science, or related field.</p>
<p><b>
Experience:</b> 1&ndash;2 years (including internships, academic projects, or full-time roles).
</p><p><b>Technical Skills:</b>
</p>
<p>&middot; <b>Programming:</b> Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow).
</p><p>&middot; <b>ML Basics:</b> Regression, classification, clustering, time-series.
</p>
<p>&middot; <b>GenAI/LLMs:</b> Prompt engineering, LangChain basics, RAG concepts.
</p><p>&middot; <b>Databases:</b> SQL (basic), exposure to NoSQL/vector DBs (Pinecone, FAISS).
</p>
<p>&middot; <b>MLOps Exposure:</b> Docker, Git, basic CI/CD knowledge.
</p><p><b>Bonus:</b> Knowledge of vision models, transformers, or cloud AI services (AWS Sagemaker, Azure ML, GCP Vertex AI)
</p><p><b>Skills Matrix</b></p>
<p>
Candidates must submit a detailed resume and fill out the following matrix:</p>
<div>
<div><div><div><p><b>Skill</b></p><br>
</div><div><p><b>Details</b></p><br>
</div><div><p><b>Skills Last Used</b></p><br>
</div><div><p><b>Experience (months)</b></p><br>
</div><div><p><b>Self-Rating (0&ndash;10)</b></p><br>
</div></div><br>
<div><div><p>Python (pandas, sklearn, PyTorch/TensorFlow)</p><br>
</div></div><br>
<p></p><div><div><p>ML Basics (Regression, Clustering, etc.)<br>
</p></div></div><br>
<p></p><div><div><p>GenAI/LLMs (Prompting, LangChain, etc.)<br>
</p></div></div><br>
<p></p><div><div><p>SQL / NoSQL<br>
</p></div></div><br>
<p></p><div><div><p>Vector Databases (FAISS, Pinecone, pgvector)<br>
</p></div></div><br>
<p></p><div><div><p>MLOps Tools (Docker, Git, CI/CD)<br>
</p></div></div><br>
<p></p><div><div><p>Cloud AI Platforms (AWS/Azure/GCP)<br>
</p></div></div><br>
</div></div><p></p><p><b>Instructions for Candidates:</b></p>
<p>
&middot; Provide a detailed resume highlighting <b>academic/industry projects in ML and AI.</b>
</p>
<p>&middot; Fill out the above skills matrix with accurate details and self-ratings.</p></div><p></p>

Junior AI Engineer

<div><div><div><p>Working Hours : <b>Full Time</b></p>
<p>Locations : <b>Hyderabad</b></p>
<p>Experience : <b>4 &ndash;6 years</b></p><p><br>
</p></div>
</div><b>About the Role:
</b><p>Soothsayer Analytics is a global AI &amp; Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning &amp; GenAI solutions&mdash;spanning predictive analytics, optimization, NLP, and enterprise-scale AI platforms&mdash;that help leading enterprises forecast, automate, and gain a competitive edge.
</p><p>As a Data Engineer, you will build the foundation that powers these AI systems&mdash;scalable, secure, and high-performance data pipelines.</p>
<p><b>Job Overview</b></p>
<p>
We seek a <b>Data Engineer (Mid-level)</b> with 4&ndash;6 years of hands-on experience in designing, building, and optimizing data pipelines. You will work closely with AI/ML teams to ensure data availability, quality, and performance for analytics and GenAI use cases.
</p>
<p><b>Key Responsibilities</b></p>
<p><b>Data Pipeline Development:</b></p>
<p>
&middot; Build and maintain scalable ETL/ELT pipelines for structured and unstructured data
</p>
<p>&middot; Ingest data from diverse sources (APIs, streaming, batch systems).
</p><p><b>Data Modeling &amp; Warehousing</b></p>
<p>
&middot; Design efficient data models to support analytics and AI workloads.
</p><p>&middot; Develop and optimize data warehouses/lakes using Redshift, BigQuery, Snowflake, or Delta Lake.
</p>
<p><b>Big Data &amp; Streaming
</b></p><p>&middot; Work with distributed systems like Apache Spark, Kafka, or Flink for real-time/large-scale data processing.
</p>
<p>&middot; Manage feature stores for ML pipelines
</p>
<p><b>Collaboration &amp; Best Practices</b></p>
<p>
&middot; Work closely with Data Scientists and ML Engineers to ensure high-quality training data.
</p>
<p>&middot; Implement data quality checks, observability, and governance frameworks.
</p>
<p><b>Required Skills &amp; Qualifications</b></p>
<p><b>
Education:</b>Bachelor&rsquo;s/Master&rsquo;s in Computer Science, Data Engineering, or related field.</p>
<p><b>
Experience:</b> 4&ndash;6 years in data engineering with expertise in:</p>
<p>
&middot; <b>Programming:</b> Python/Scala/Java (Python preferred).
</p>
<p>&middot; <b>Big Data &amp; Processing:</b> Apache Spark, Kafka, Hadoop.
</p>
<p>&middot; <b>Databases:</b> SQL/NoSQL (Postgres, MongoDB, Cassandra).
</p><p>&middot; <b>Data Warehousing:</b> Snowflake, Redshift, BigQuery, or similar.
</p>
<p>&middot; <b>Orchestration:</b> Airflow, Luigi, or similar.
</p>
<p>&middot; <b>Cloud Platforms:</b> AWS, Azure, or GCP (data services).
</p>
<p>&middot; <b>Version Control &amp; CI/CD:</b> Git, Jenkins, GitHub Actions.
</p>
<p>&middot; <b>MLOps/GenAI pipelines:</b> (feature engineering, embeddings, vector DBs)
</p>
<p><b>Skills Matrix</b></p>
<p>
Candidates must submit a detailed resume and fill out the following matrix:</p>
<div>
<div><div><div><p><b>Skill</b></p><br>
</div><div><p><b>Details</b></p><br>
</div><div><p><b>Skills Last Used</b></p><br>
</div><div><p><b>Experience (months)</b></p><br>
</div><div><p><b>Self-Rating (0&ndash;10)</b></p><br>
</div></div><br>
<div><div><p>Python</p><br>
</div></div><br>
<p></p><div><div><p>SQL / NoSQL</p><br>
</div></div><br>
<p></p><div><div><p>Apache Spark</p><br>
</div></div><br>
<p></p><div><div><p>Kafka</p><br>
</div></div><br>
<p></p><div><div><p>Data Warehousing (Snowflake, Redshift, etc.)<br>
</p></div></div><br>
<p></p><div><div><p>Orchestration (Airflow, Luigi, etc.)<br>
</p></div></div><br>
<p></p><div><div><p>Cloud (AWS / Azure / GCP)<br>
</p></div></div><br>
<p></p><div><div><p>Data Quality / Governance Tools<br>
</p></div></div><br>
<p></p><div><div><p>MLOps / LLMOps</p><br>
</div></div><br>
<p></p><div><div><p>GenAI Integration</p><br>
</div></div><br>
</div></div><p></p><p><b>Instructions for Candidates:</b></p>
<p>
&middot; Provide a detailed resume highlighting <b>end-to-end data engineering projects.</b>
</p>
<p>&middot; Fill out the above <b>skills matrix</b> with accurate dates, duration, and self-ratings.</p></div><p></p>

Data Engineer

Join Soothsayer Analytics and explore 3   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Soothsayer Analytics

ABOUT_COMPANY

Senior Cloud Engineer

Junior AI Engineer

Data Engineer