Responsibilities Design and develop high-scale data architectures, considering factors such as performance, scalability, reliability, and maintainability. Architect and implement database designs from scratch, optimizing for query performance and data integrity. Leverage your deep understanding of web concepts, web services, asynchronous and parallel processing, and database connectivity to create efficient data pipelines. Apply data science principles to solve complex business problems and drive data-driven decision-making. Work with Large Language Models (LLMs) to develop intelligent data applications. Write clean, well-tested code and contribute to the development of automated testing frameworks. Manage large databases and indexes in production environments. Develop and execute data crawling strategies to extract relevant data from various sources. Collaborate with cross-functional teams to ensure alignment with business objectives and technical requirements. We need you to have: Must-Have 8+ years of experience in data related field. Strong foundation in computer science, with expertise in data structures, algorithms, and software design. Hands-on experience with Python for data analysis and development. Proven ability to design and implement high-scale data architectures. Deep understanding of web concepts, web services, asynchronous and parallel processing, and database connectivity. Knowledge of data science principles and their application in data engineering. Experience working with Large Language Models (LLMs). Proficiency in unit testing and automation test case development. Experience managing large databases or indexes in production environments. Data crawling experience. Good-to-Have Experience with MongoDB, ElasticSearch, or Neo4J. Familiarity with Flask, Django, Express, or microservices architectures. Exposure to Hadoop, Spark, or Kafka. Experience with SCM tools (branching, CI, deployment using Jenkins). Cloud experience with GCP, AWS, or Azure. You will be based out of: Pune, India.
Job Title: AI & Full Stack Technology Expert Product Engineering Role Overview: The company is seeking a self driven AI & Tech expert with full-stack development skills to architect, develop, and scale next-generation AI-powered products and client deliveries. This role combines deep technical expertise with product thinking, empowering the delivery of robust, data-driven solutions that directly impact user experience and business growth. Role & responsibilities Responsibilities Lead the design, development, and deployment of AI-driven web and product applications. Build highly-scalable front-end and back-end systems, integrating AI/ML models and algorithms to deliver new product functionality. Collaborate closely with product managers, data scientists, engineers, and designers to translate product requirements into technical solutions. Own the product architecture, ensuring high performance, reliability, and maintainability throughout the stack. Participate in the full product lifecycle: ideation, prototyping, MVP development, scaling, testing, and continuous improvement. Mentor junior talent and foster best practices in code quality, design patterns, and innovation. Keep up-to-date with the latest advances in AI, web development, and cloud technologies, contributing to the technical roadmap. Required Qualifications Bachelors/Masters degree in Computer Science, Engineering, or related field. Proven experience in full stack development: front-end frameworks (React, Angular, Vue), back-end stacks (Python, Node.js, Java), and cloud deployment. Practical experience integrating AI/ML modules and building data-powered product features. Experience in a product company, working within cross-functional teams focused on user-centric design. Deep expertise with REST APIs, microservices, containerization (Docker, Kubernetes), and CI/CD pipelines. Solid understanding of product development lifecycles, agile methodologies, and metrics-driven improvement. Nice to Have Demonstrated impact in building and scaling AI-powered products in fast-paced environments. Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn), cloud platforms (AWS, Azure, GCP), and data engineering tools. Experience in product roadmap planning, requirements analysis, and user research. Interested candidates can apply at smriti.mishra@partex.ai
****Please Note: Only candidates whose qualifications closely match the job description requirements will be contacted for the next stage of the recruitment process.***** Key Responsibilities 1. Scientific Program Coordination (Computational & AI-Focused) Manage multi-disciplinary discovery initiatives that span protein engineering, target assessment, sequence-level modeling, generative molecule design, and multi-omics interpretation. Assist in defining project scopes, scientific milestones, and computational deliverables. Track progress across ML modeling, bioinformatics analyses, structural modeling cycles, and insight-generation workflows. Ensure scientific alignment across computational biology, cheminformatics, ML engineering, and platformdevelopment teams. 2. Protein Engineering & Structural Biology Support Coordinate computational strategies related to: protein sequence modeling and mutational scanning structure prediction (AlphaFold, ESMFold, Rosetta-based workflows) binding interface characterization and proteinligand or proteinprotein interactions stability/solubility prediction, developability assessments, and variant prioritization Review structural models, annotated residue-level insights, and design outputs generated by ML/AIplatforms. Integrate sequence- and structure-based computational results into program-level decision-making. 3. Multi-Omics & Bioinformatics Workflow Management Collaborate with computational biologists to design and support analyses involving transcriptomics,proteomics, single-cell data, and genome-wide datasets. Aid in selection and interpretation of: differential expression analyses pathway enrichment and network biology modeling biomarker discovery and target prioritization from omics datasets Organize pipelines for integrating omics-derived insights with AI predictions, structural features, and medicinal chemistry landscapes. 4. Data Integration & ML Problem Formulation Translate biological/scientific questions into machine learning tasks such as: protein fitness prediction generative sequence design ligandtarget affinity prediction ADMET or MoA modeling large-scale virtual screening Collaborate with ML engineers on dataset preparation, feature construction, labeling strategies, and model evaluation. Review outputs from predictive models and convert them into actionable insights for discovery teams. 5. Computational Workflow Development & Documentation Maintain structured, reproducible workflows across structural modeling, omics analysis, genetic variant scoring, and ML-driven design loops. Evaluate and help standardize emerging computational tools, databases, and algorithms relevant to protein science, bioinformatics, and ML. Develop and maintain internal documentation, pathway diagrams, decision frameworks, and best-practice guidelines. 6. Scientific Communication & Cross-Functional Liaison Prepare clear, data-rich scientific briefs, platform feature requests, white papers, and internal strategy documents. Present computational insights to scientific leadership, domain experts, and cross-functional stakeholders. Support manuscripts, conference presentations, grant applications, and external scientific communication. Required Qualifications Educational Background Masters/PhD in Computational Biology, Bioinformatics, Protein Engineering, Structural Biology, Systems Biology, Computational Chemistry, or a related discipline. 2–5 years experience in dry-lab drug discovery, computational R&D, or AI/ML-driven biological research, or postdoctoral research in a relevant computational/biological field. Strong knowledge in at least two of the following areas and familiarity with the others: Protein engineering (sequence analysis, mutational scanning, stability modeling) Structural biology (structure prediction, docking, molecular interactions) Multi-omics analytics (RNA-seq, proteomics, scRNA-seq, integrative systems biology) Bioinformatics pipelines (annotation, variant analysis, network biology, functional genomics) ML-driven molecular/property prediction Ability to interpret sequence/structure/omics outputs and contextualize them within drug discoveryworkflows. Excellent scientific communication and project coordination abilities.
Role & responsibilities Execute a comprehensive sales strategy aligned with the company's overall business objectives for the US and European markets. Responsible for entire sales cycle from Lead generation to deal closing with monthly, quarterly and annual sales and revenue targets Prospecting via multi channel approach and using sales intelligence tools (backend / virtual), Lead profiling and qualification using Sales intelligence tool Zymewire, Zoominfo Pitching via multi channel approach (email, Linkedin, cold calling other) Engaging with customers to understand their needs and ensure timely, accurate and relevant responses to customer inquiries and requests for information. Maintaining a high level of responsiveness. Tracking and updating progress and pipeline of Leads and deals timely in CRM tool Consolidate weekly numbers for all outreaches vs. qualified leads to generate conversion stats for cold outreach Share weekly Highlights and Lowlights Preferred candidate profile Bachelor's/Masters degree in business, marketing, or a related field. Minimum of 10 years of experience in inside sales leadership roles, with a proven track record of success in building and scaling high-performing teams. Demonstrated ability to develop and execute successful sales strategies proven track record of exceeding lead generation and deal closure goals Excellent communication and interpersonal skills, with the ability to effectively communicate with all levels of the organization. Proven ability to analyze data and use insights to drive business decisions. Experience with CRM and sales enablement tools. Strong understanding of the sales process and the ability to optimize it for maximum efficiency and effectiveness. Align diverse team targets, collect data, and track progress for continuous improvement. Proven ability to accurately capture, organize, and analyze data to ensure efficient progress tracking. Cross Entity/Geography Collaboration, Reporting & Governance Energetic and Focused **Note: Please note that only profiles closely aligned with the JD will be considered for the next stage. To help us evaluate your fit more accurately, kindly provide responses to the screening questions.
About the Role We are looking for a Senior AI Engineer to join our AI Team. In this role, you will lead a small sub-team and drive the development of production-grade AI solutions, with a heavy focus on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). You will own the full project lifecycle, ensuring that complex clinical data is structured and accessible through cutting-edge AI methodologies. Key Responsibilities Technical Leadership: Direct a small sub-team of engineers and scientists, overseeing project delivery, code reviews, and technical mentorship in an Agile environment. LLM & RAG Implementation: Architect and deploy advanced RAG pipelines and LLM-based solutions, including instruction fine-tuning and prompt engineering. Advanced Modeling: Implement Deep Learning and NLP models to extract insights and structure clinical data from vast, unstructured datasets. System Architecture: Design high-performance data pipelines and manage large-scale NoSQL/Graph databases (e.g., MongoDB, Elastic Search, Neo4J). Strategic Collaboration: Partner with medical experts and commercial teams to integrate AI into healthcare digitization and contribute to company IP (patents/whitepapers). Requirements Experience: 6+ years in Data Science or Data Engineering with experience leading technical sub-teams. AI Expertise: Deep proficiency in LLMs, RAG architectures , and Deep Learning frameworks ( PyTorch or TensorFlow ). Python Mastery: Superior coding skills with deep knowledge of data structures, algorithms, and software design principles. Data Infrastructure: Hands-on experience with NoSQL/Graph DBs , async/parallel processing, and information retrieval. Engineering Rigor: Experience with software development best practices, unit testing, and deployment using Docker/Kubernetes . Education: Degree (Master/PhD preferred) in Computer Science, Mathematics, Physics, or Engineering. Language: Professional proficiency in both English and German is compulsory. Nice-to-Have Experience with Scrapy and distributed web crawling at scale. Familiarity with Big Data tools like Hadoop, Spark, or Kafka . Previous exposure to the Atlassian suite (JIRA) and Agile methodologies